Diabetes management therapy advisor

ABSTRACT

A method includes obtaining training data for a plurality of patients of a patient population. The training data includes training blood glucose history data including treatment doses of insulin administered by the patients of the patient population and one or more outcome attributes associated with each treatment dose. The method also includes identifying, for each patient of the patient population, one or more optimum treatment doses of insulin from the treatment doses yielding favorable outcome attributes. The method also includes receiving patient-state information for the treated patient, determining a next recommended treatment dose of insulin for the treated patient based on one or more of the identified optimum treatment doses associated with the patients of the patient population having training patient-state information similar to the patient-state information for the treated patient, and transmitting the next recommended treatment dose to a portable device associated with the treated patient.

CROSS REFERENCE TO RELATED APPLICATIONS

This U.S. patent application is a continuation of, and claims priorityunder 35 U.S.C. § 120 from, U.S. patent application Ser. No. 15/241,703,filed on Aug. 19, 2016, which claims priority under 35 U.S.C. § 119(e)to U.S. Provisional Application 62/207,613, filed Aug. 20, 2015. Thedisclosures of these prior applications are considered part of thedisclosure of this application and are hereby incorporated by referencein their entireties.

TECHNICAL FIELD

This disclosure relates to a diabetes management therapy advisor foridentifying and optimizing personalized therapies for the treatment ofDiabetes Mellitus.

BACKGROUND

According to the most recent data from the American Diabetes Associationand CDC, more than 29 million Americans have diabetes. But even moreimportantly, another 86 million—or one in three—have pre-diabetes.Without effective intervention, it is estimated that 15%-20% of theseindividuals will develop diabetes within five years. The InternationalDiabetes Federation reports the global incidence of diabetes at 387million people. It is expected that this number will grow to 592 millionpeople over the next 20 years.

Diabetics often have higher rates of cardiovascular, renal,gastrointestinal, neurological, thyroid diseases, and ophthalmologicalcomplications compared to people without diabetes. Patients withdiabetes often may receive a wide array of medications includinginjectable long-acting and rapid-acting insulins, inhaled insulin, oralmedications, and other injectable anti-diabetic medications. Among thedifferent classes of medications, some medications are contraindicatedfor pregnancy or patients with severe kidney disease. Moreover, othermedications which are appropriate for the treatment of Type 2 diabetes,are contraindicated for Type 1 diabetes.

SUMMARY

One aspect of the disclosure provides a method for determining atreatment dose for a treated patient. The method includes obtaining, atdata processing hardware, training data for a plurality of patients of apatient population from memory hardware in communication with the dataprocessing hardware. The training data includes training blood glucosehistory data and training patient-state information for each patient ofthe patient population. The training blood glucose history data includestreatment doses of insulin administered by the patients of the patientpopulation and one or more outcome attributes associated with eachtreatment dose of insulin administered by the patients of the patientpopulation. For each patient of the patient population, the methodincludes identifying, using the data processing hardware, one or moreoptimum treatment doses of insulin from the treatment doses of insulinyielding favorable outcome attributes. The method also includesreceiving, at the data processing hardware, patient-state informationfor the treated patient. The method also includes determining, using thedata processing hardware, a next recommended treatment dose of insulinfor the treated patient based on one or more of the identified optimumtreatment doses associated with the patients of the patient populationhaving training patient-state information similar to the patient-stateinformation for the treated patient. The method further includestransmitting the next recommended treatment dose to a portable deviceassociated with the treated patient, the portable device displaying thenext recommended insulin dose.

Implementations of the disclosure may include one or more of thefollowing optional features. In some implementations, obtaining thetraining data includes obtaining the training data automatically at anend of a re-occurring configurable time interval. Obtaining the trainingdata may include obtaining the training data immediately in response toa user input selecting an immediate start button displayed upon adisplay in communication with the data processing hardware. Obtainingthe training data may also include obtaining the training data on aselected date.

In some examples, determining the next recommended treatment dose ofinsulin for the treated patient includes determining the treated patientrequires insulin based on the patient-state information for the treatedpatient and receiving meal boluses of insulin previously administered bythe treated patient during a scheduled time-interval. Determining a nextrecommended meal bolus during the scheduled time interval for thetreated patient may be based on at least one of the identified optimumtreatment doses associated with the scheduled time interval and thereceived meal boluses of insulin previously administered by the treatedpatient during the scheduled time interval. The scheduled time intervalmay include a pre-breakfast time interval, a pre-lunch time interval, apre-dinner time interval, a bedtime time interval, or a midsleep timeinterval.

One or more outcome attributes of the training blood glucose historydata may include a blood glucose percent error based on a function of anext scheduled blood glucose measurement and a blood glucose targetrange. The next scheduled blood glucose measurement may correspond to ablood glucose measurement occurring after administration of acorresponding treatment dose of insulin. Determining the nextrecommended treatment dose of insulin for the treated patient mayinclude determining the treated patient requires insulin based on thepatient-state information for the treated patient and receiving basaldoses of insulin previously administered by the treated patient.Determining a next recommended basal dose for the treated patient may bebased on at least one of the identified optimum treatment doses and thereceived basal doses of insulin previously administered by the treatedpatient.

In some examples, the method includes transmitting the next recommendedtreatment dose of insulin to an administration device in communicationwith the data processing hardware. The administration device may includea doser and an administration computing device in communication with thedoser. The administration computing device may cause the doser toadminister insulin specified by the next recommended treatment dose ofinsulin. Determining the next recommended treatment dose for thetreatment patient may include determining anti-diabetic medications areusable for treating the treated patient based on the patient-stateinformation for the treated patient, receiving a glycated hemoglobinmeasurement of the patient, and determining an anti-diabetes medicationregimen for the treated patient based on the glycated hemoglobinmeasurement and the training data.

The treatment doses of the training data may correspond to anti-diabetesmedication dose-combinations administered by patients of the patientpopulation. The outcome attributes of the training data may correspondto a glycated hemoglobin measurement associated with each anti-diabetesmedication regimen. The patient-state information may include aplurality of patient-state attributes associated with the patient. Thepatient-state attributes may include one or more of an age, a gender, amedical history, a body mass index, a medical history, risk factors,and/or financial attributes.

Another aspect of the disclosure provides a system for determining atreatment dose for a treated patient. The system includes a dosingcontroller including data processing hardware and memory hardware incommunication with the data processing hardware. The dosing controllerobtains training data for a plurality of patients of a patientpopulation from the memory hardware. The training data includes trainingblood glucose history data and training patient-state information foreach patient of the patient population. The training blood glucosehistory data includes treatment doses of insulin administered by thepatients of the patient population and one or more outcome attributesassociated with each treatment dose of insulin administered by thepatients of the patient population. For each patient of the patientpopulation, the system includes identifying one or more optimumtreatment doses of insulin from the treatment doses of insulin yieldingfavorable outcome attributes and receiving patient-state information forthe treated patient. The system also includes determining a nextrecommended treatment dose of insulin for the treated patient based onone or more of the identified optimum treatment doses associated withthe patients of the patient population having training patient-stateinformation similar to the patient-state information for the treatedpatient. The system further includes transmitting the next recommendedtreatment dose to a portable device associated with the treated patient,the portable device displaying the next recommended insulin dose.

This aspect may include one or more of the following optional features.In some implementations, obtaining the training data includes obtainingthe training data automatically at an end of a re-occurring configurabletime interval. Obtaining the training data may include obtaining thetraining data immediately in response to a user input selecting animmediate start button displayed upon a display in communication withthe data processing hardware. Obtaining the training data may furtherinclude obtaining the training data on a selected date.

In some examples, determining the next recommended treatment dose ofinsulin for the treated patient comprises determining the treatedpatient requires insulin based on the patient-state information for thetreated patient and receiving meal boluses of insulin previouslyadministered by the treated patient during a scheduled time-interval.Determining a next recommended meal bolus during the scheduled timeinterval for the treated patient may be based on at least one of theidentified optimum treatment doses associated with the scheduled timeinterval and the received meal boluses of insulin previouslyadministered by the treated patient during the scheduled time interval.The scheduled time interval may include a pre-breakfast time interval, apre-lunch time interval, a pre-dinner time interval, a bedtime timeinterval, or a midsleep time interval. The one or more outcomeattributes of the training blood glucose history data may include ablood glucose percent error based on a function of a next scheduledblood glucose measurement and a blood glucose target range. The nextscheduled blood glucose measurement may correspond to a blood glucosemeasurement occurring after administration of a corresponding treatmentdose of insulin.

Determining the next recommended treatment dose of insulin for thetreated patient may include determining the treated patient requiresinsulin based on the patient-state information for the treated patientand receiving basal doses of insulin previously administered by thetreated patient. Determining a next recommended basal dose for thetreated patient may be based on at least one of the identified optimumtreatment doses and the received basal doses of insulin previouslyadministered by the treated patient.

In some examples, the dosing controller may transmit the nextrecommended treatment dose of insulin to an administration device incommunication with the dosing controller. The administration device mayinclude a doser and an administration device in communication with thedoser. The administration computing device may cause the doser toadminister insulin specified by the next recommended treatment dose ofinsulin.

In some implementations, determining the next recommended treatment dosefor the treated patient includes determining anti-diabetic medicationsare usable for treating the treated patient based on the patient-stateinformation for the treated patient, receiving a glycated hemoglobinmeasurement of the patient, and determining an anti-diabetes medicationregimen for the treated patient based on the glycated hemoglobinmeasurement and the training data.

The treatment doses of the training data may correspond to anti-diabetesmedication dose-combinations administered by patients of the patientpopulation. The outcome attributes of the training data may correspondto a glycated hemoglobin measurement associated with each anti-diabetesmedication regimen. The patient-state information may include aplurality of patient-state attributes associated with the patient. Thepatient-state attributes may include one or more of an age, a gender, amedical history, a body mass index, a medical history, risk factors,and/or financial attributes.

The details of one or more implementations of the disclosure are setforth in the accompanying drawings and the description below. Otheraspects, features, and advantages will be apparent from the descriptionand drawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1A is a schematic view of an exemplary system for recommendingtreatment doses for a patient.

FIG. 1B is a schematic view of an exemplary system for recommendingtreatment doses for a patient.

FIG. 1C is a schematic view of an exemplary administration device incommunication with a dosing controller.

FIG. 2A is a schematic view of an exemplary process for starting apatient treatment program for a patient.

FIGS. 2B and 2C are schematic views of an exemplary display forinputting patient information.

FIG. 2D is a schematic view of an exemplary display for selecting astart mode of a training program.

FIG. 3A is a schematic view of an exemplary patient training program foradjusting doses of insulin.

FIG. 3B is a schematic view of an exemplary patient treatment programusing training data for determining an optimum dose of insulin.

FIG. 3C is a schematic view of an exemplary anti-diabetes program forprescribing Anti-Diabetes Medication dose-combinations to a patient.

FIG. 4A is a schematic view of an exemplary subcutaneous meal bolusadjustment program.

FIG. 4B is a schematic view of an exemplary subcutaneous basaladjustment program.

FIG. 4C is a schematic view of an anti-diabetes medication adjustmentprogram.

FIG. 5 is an exemplary arrangement of operations for determining atreatment dose for a treated patient.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

Diabetes requires early, continuous, effective, and regular treatment tosignificantly delay, and in some instances eliminate, the progression ofthe disease. For non-specialist healthcare providers, managing diabetescan be an extremely complex process. Patients with diabetes are oftenprescribed a large number of different medications for dyslipidemia,hypertension, and control of blood glucose. Diuretic medications oftenused to treat heart failure, blood pressure, and other kidney disorders,may have an unintended side effect that contributes to hypoglycemia.Healthcare professions must weigh effects and potential pharmacodynamicsand pharmacokinetic interactions of concomitant medications.

Referring to FIGS. 1A-1C, in some implementations, a clinical decisionsupport system 100 analyzes inputted patient condition parameters for apatient 10 and calculates a personalized dose of insulin to bring andmaintain the patient's blood glucose level into a target range BG_(TR).As used herein, the patient 10 may refer to an outpatient that may belocated at some remote location, such as the patient's 10 residence orplace of employment, or to an inpatient located at a clinic 42 orhospital. Moreover, the system 100 monitors the glucose levels of apatient 10 and calculates a recommended subcutaneous insulin dose tobring the patient's blood glucose into the preferred target rangeBG_(TR) over a recommended period of time. In some implementations, thesystem 100 monitors glycated hemoglobin (hereinafter ‘A1c’) levels of apatient 10 and calculates a recommended dose of one or moreAnti-Diabetes Medications (ADMs) to significantly delay, and in someinstances eliminate, the progression of diabetes in the patient 10. Asused herein, ADMs refer to non-insulin medications that may beadministered to the patient. A qualified and trained healthcareprofessional 40 may use the system 100 along with clinical reasoning todetermine the proper dosing (e.g., insulin dosing, ADM dosing, or acombination of the two) administered to a patient 10. Therefore, thesystem 100 is a glycemic management tool for evaluation of a patient'scurrent and cumulative blood glucose value BG, or current and cumulativeA1c level, while taking into consideration the patient's informationsuch as age, weight, and height. The system 100 may also consider otherinformation such as carbohydrate content of meals and/or insulin dosesbeing administered to the patient 10, e.g., long-acting insulin dosesfor basal insulin and rapid-acting insulin doses for meal boluses andcorrection boluses. Based on those measurements (that may be stored innon-transitory memory 24, 114, 144), the system 100 recommends asubcutaneous basal and bolus insulin dosing recommendation or prescribeddose to adjust and maintain the blood glucose level towards aconfigurable (based on the patient's information) physician's determinedblood glucose target range BG_(TR). The system 100 also considers apatient's insulin sensitivity or improved glycemic management andoutcomes. In some examples, the system 100 recommends an ADM dosingrecommendation treatment to adjust and maintain the A1c level of thepatient 10 towards a configurable (based on the patient's information)physician's determined A1c target range A1c_(TR).

The system 100 may take into account pertinent patient information suchas patient-state information and blood glucose BG history dataassociated with the patient 10. The system 100 may include a domainknowledge base including information about diabetes (that may be storedin non-transitory memory 24, 114, 144), including pertinent informationabout Type 1 diabetes mellitus (hereinafter ‘DM1’) and Type 2 diabetesmellitus (hereinafter ‘DM2’). The domain knowledge base may also includeinformation associated with effects and potential pharmacodynamics andpharmacokinetic interactions of concomitant medications. Based on theinformation of the domain knowledge base and the patient-stateinformation and/or the BG history data associated with the patient 10,the system 100 may select a personalized diabetes treatment therapy forglycemic control of the patient 10. The system 100 may also store (innon-transitory memory 24, 114, 144) training blood glucose BG data andpatient-state information for a patient population, and use the trainingBG data for patients of the patient population that have similarpatient-state attributes as the patient 10 to adjust a subcutaneousbasal and bolus insulin dosing recommendation (or an ADM dosingrecommendation) associated with the patient 10.

Finally, the system 100 provides a reporting platform for reporting therecommendations, adjustments, or prescribed dose(s) to the user 40 andthe patient 10. In addition, the system 100 provides faster, morereliable, and more efficient insulin administration than a humanmonitoring the insulin administration. The system 100 reduces humanoversight in prescribing medications that may contribute to unintendedside effects of hypoglycemia or hyperglycemia due to the system'scapability of accessing the information of the domain knowledge base.The system 100 reduces the probability of human error and insuresconsistent treatment, due to the system's capability of storing andtracking the patient's blood glucose levels BG, which may be used forstatistical studies. The system 100 provides a meal-by-meal adjustmentof Meal Boluses without carbohydrate counting, by providing a dedicatedsubprogram that adjusts meal boluses based on the immediately precedingmeal bolus and the BG that followed it. The system 100 provides ameal-by-meal adjustment of Meal Boluses with carbohydrate counting byproviding a dedicated subprogram that adjusts meal boluses based aCarbohydrate-to-Insulin Ratio (CIR) that is adjusted at each meal, basedon the CIR used at the immediately preceding meal bolus and the BG thatfollowed it.

Hyperglycemia is a condition that exists when blood sugars are too high.While hyperglycemia is typically associated with diabetes, thiscondition can exist in many patients who do not have diabetes, yet haveelevated blood sugar levels caused by trauma or stress from surgery andother complications from hospital procedures. Insulin therapy is used tobring blood sugar levels back into a normal range.

Hypoglycemia may occur at any time when a patient's blood glucose levelis below a preferred target. Appropriate management of blood glucoselevels for critically ill patients reduces co-morbidities and isassociated with a decrease in infection rates, length of hospital stay,and death. The treatment of hyperglycemia may differ depending onwhether or not a patient has been diagnosed with DM1, DM2, gestationaldiabetes mellitus, or non-diabetic stress hyperglycemia. The bloodglucose target range BG_(TR) is defined by a lower limit, i.e., a lowtarget BG_(TRL) and an upper limit, i.e., a high target BG_(TRH).

Diabetes Mellitus has been treated for many years with insulin. Somerecurring terms and phrases are described below:

Injection: Administering insulin by means of manual syringe or aninsulin “pen,” with a portable syringe named for its resemblance to thefamiliar writing implement.

Infusion: Administering insulin in a continuous manner by means of aninsulin pump for subcutaneous insulin apparatus 123, 123 a capable ofcontinuous administration.

Basal-Bolus Therapy: Basal-bolus therapy is a term that collectivelyrefers to any insulin regimen involving basal insulin and boluses ofinsulin.

Basal Insulin: Insulin that is intended to metabolize the glucosereleased by a patient's the liver during a fasting state. Basal insulinis administered in such a way that it maintains a background level ofinsulin in the patient's blood, which is generally steady but may bevaried in a programmed manner by an insulin pump 123 a. Basal insulin isa slow, relatively continuous supply of insulin throughout the day andnight that provides the low, but present, insulin concentrationnecessary to balance glucose consumption (glucose uptake and oxidation)and glucose production (glucogenolysis and gluconeogenesis). A patient'sBasal insulin needs are usually about 10 to 15 mU/kg/hr and account for30% to 50% of the total daily insulin needs; however, considerablevariation occurs based on the patient 10.

Bolus Insulin: Insulin that is administered in discrete doses. There aretwo main types of boluses, Meal Bolus and Correction Bolus.

Meal Bolus: Taken just before a meal in an amount which is proportionalto the anticipated immediate effect of carbohydrates in the mealentering the blood directly from the digestive system. The amounts ofthe Meal Boluses may be determined and prescribed by a physician 40 foreach meal during the day, i.e., breakfast, lunch, and dinner.Alternatively, the Meal Bolus may be calculated in an amount generallyproportional to the number of grams of carbohydrates in the meal. Theamount of the Meal Bolus is calculated using a proportionality constant,which is a personalized number called the Carbohydrate-to-Insulin Ratio(CIR) and calculated as follows:Meal Insulin Bolus={grams of carbohydrates in the meal}/CIR  (1)

Correction Bolus CB: Injected immediately after a blood glucosemeasurement; the amount of the correction bolus is proportional to theerror in the BG (i.e., the bolus is proportional to the differencebetween the blood glucose measurement BG and the patient's personalizedTarget blood glucose BG_(Target)). The proportionality constant is apersonalized number called the Correction Factor, CF, and is calculatedas follows:CB=(BG−BG_(Target))/CF  (2)

A Correction Bolus CB is generally administered in a fasting state,after the previously consumed meal has been digested. This oftencoincides with the time just before the next meal.

In some implementations, blood glucose measurements BG are aggregatedusing an exponentially-weighted moving average EMA_(t) as a function foreach modal day's time interval BG. The EMAt is calculated as follows:EMA_(t)=α(BG_(t))+(1−α)EMA_(t-1),  (3)wherein:α=2/(n+1),wherein n is the number of equivalent days averaged. In otherembodiments, an arithmetic moving average is utilized that calculatesthe sum of all BG values in n days divided by a total count (n) of allvalues associated with the arithmetic average.

There are several kinds of Basal-Bolus insulin therapy including InsulinPump therapy and Multiple Dose Injection therapy:

Insulin Pump Therapy: An insulin pump 123 a is a medical device used forthe administration of insulin in the treatment of diabetes mellitus,also known as continuous subcutaneous insulin infusion therapy. Thedevice includes: a pump, a disposable reservoir for insulin, and adisposable infusion set. The pump 123 a is an alternative to multipledaily injections of insulin by insulin syringe or an insulin pen andallows for intensive insulin therapy when used in conjunction with bloodglucose monitoring and carbohydrate counting. The insulin pump 123 a isa battery-powered device about the size of a pager. It contains acartridge of insulin, and it pumps the insulin into the patient via an“infusion set”, which is a small plastic needle or “canula” fitted withan adhesive patch. Only rapid-acting insulin is used.

Multiple Dose Injection (MDI): MDI involves the subcutaneous manualinjection of insulin several times per day using syringes or insulinpens 123 b. Meal insulin is supplied by injection of rapid-actinginsulin before each meal in an amount proportional to the meal. Basalinsulin is provided as a once, twice, or three time daily injection of adose of long-acting insulin. Other dosage frequencies may be available.Advances continue to be made in developing different types of insulin,many of which are used to great advantage with MDI regimens:

Long-acting insulins are non-peaking and can be injected as infrequentlyas once per day. These insulins are widely used for Basal Insulin. Theyare administered in dosages that make them appropriate for the fastingstate of the patient, in which the blood glucose is replenished by theliver to maintain a steady minimum blood glucose level.

Rapid-acting insulins act on a time scale shorter than natural insulin.They are appropriate for boluses.

The clinical decision support system 100 includes a glycemic managementmodule 50, an integration module 60, a surveillance module 70, and areporting module 80. Each module 50, 60, 70, 80 is in communication withthe other modules 50, 60, 70, 80 via a network 20. In some examples, thenetwork 24 (discussed below) provides access to cloud computingresources that allows for the performance of services on remote devicesinstead of the specific modules 50, 60, 70, 80. The glycemic managementmodule 50 executes a process 200 (e.g., an executable instruction set)on a processor 112, 132, 142 or on the cloud computing resources. Theintegration module 60 allows for the interaction of users 40 andpatients 10 with the system 100. The integration module 60 receivesinformation inputted by a user 40 and allows the user 40 to retrievepreviously inputted information stored on a storage system (e.g., one ormore of cloud storage resources 24, a non-transitory memory 144 of aclinic's electronic medical system 140, a non-transitory memory 114 ofthe patient device 110, or other non-transitory storage media incommunication with the integration module 60). Therefore, theintegration module 60 allows for the interaction between the users 40,patients 10, and the system 100 via a display 116, 146. In someexamples, integration module 60 allows the user 40 or patient 10 toinput blood glucose history data 208 b associated with the patient 10for storage on the storage system 24, 144, 114. The surveillance module70 considers patient state information 208 a received from a user 40 viathe integration module 60 and information received from a glucometer 124that measures a patient's blood glucose value BG and determines if thepatient 10 is within a threshold blood glucose value BG_(TH). In someexamples, the surveillance module 70 alerts the user 40 if a patient'sblood glucose values BG are not within a threshold blood glucose valueBG_(TH). The surveillance module 70 may be preconfigured to alert theuser 40 of other discrepancies between expected values and actual valuesbased on pre-configured parameters (discussed below). For example, whena patient's blood glucose value BG drops below a lower limit of thethreshold blood glucose value BG_(THL). The reporting module 80 may bein communication with at least one display 116, 146 and providesinformation to the user 40 determined using the glycemic managementmodule 50, the integration module 60, and/or the surveillance module 70.In some examples, the reporting module 80 provides a report that may bedisplayed on a display 116, 146 and/or is capable of being printed.

The system 100 is configured to evaluate a glucose level and nutritionalintake of a patient 10. Based on the evaluation and analysis of thedata, the system 100 calculates an insulin dose, which is administeredto the patient 10 to bring and maintain the blood glucose level of thepatient 10 into the blood glucose target range BG_(TR). The system 100may be applied to various devices, including, but not limited to,subcutaneous insulin infusion pumps 123 a, insulin pens 123 b,glucometers 124, continuous glucose monitoring systems, and glucosesensors.

In some implementations, the system 100 considers outcome dataassociated with an insulin dose administered to the patient 10. Theoutcome data may include a next scheduled blood glucose measurementBGnext showing the effect of the insulin dose previously administered tothe patient 10. Generally, the BGnext occurs a sufficient amount of time(e.g., four to six hours) after the insulin dose is administered to thepatient after the effects of the insulin and food are both complete sothat the BGnext indicates the precision of the recommended dose. Forexample, the system 100 may adjust a next recommended insulin dose byincreasing the dose when the BGnext is greater than a target centerBG_(TC) of the blood glucose target range BG_(TR), or decreasing thedose when the BGnext is less than the target center BG_(TC). The nextrecommended insulin dose may include a next scheduled meal bolus afterthe administered insulin dose or a meal bolus associated with theadministered insulin dose, but on the next day.

In some examples the clinical decision support system 100 includes anetwork 20, a patient device 110, a dosing controller 160, a serviceprovider 130, and a meter manufacturer provider 160. The patient device110 may include, but is not limited to, desktop computers 110 a orportable electronic device 110 b (e.g., cellular phone, smartphone,personal digital assistant, barcode reader, personal computer, or awireless pad) or any other electronic device capable of sending andreceiving information via the network 20. In some implementations, oneor more of the patient's glucometer 124, insulin pump 123 a, or insulinpen 123 b are capable of sending and receiving information via thenetwork 20.

The patient device 110 a, 110 b includes a data processor 112 a, 112 b(e.g., a computing device that executes instructions), andnon-transitory memory 114 a, 114 b and a display 116 a, 116 b (e.g.,touch display or non-touch display) in communication with the dataprocessor 112. In some examples, the patient device 110 includes akeyboard 118, speakers 212, microphones, mouse, and a camera.

The glucometer 124, insulin pump 123 a, and insulin pen 123 b associatedwith the patient 10 include a data processor 112 c, 112 d, 112 e (e.g.,a computing device that executes instructions), and non-transitorymemory 114 c, 114 d, 114 e and a display 116 c, 116 d, 116 e (e.g.,touch display or non-touch display in communication with the dataprocessor 112 c, 112 d, 112 e.

The meter manufacturer provider 190 may include may include a dataprocessor 192 in communication with non-transitory memory 194. The dataprocessor 192 may execute a proprietary download program 196 fordownloading blood glucose BG data from the memory 114 c of the patient'sglucometer 124. In some implementations, the proprietary downloadprogram 196 is implemented on the health care provider's 140 computingdevice 142 or the patient's 10 device 110 a for downloading the BG datafrom memory 114 c. In some examples, the download program 196 exports aBG data file for storage in the non-transitory memory 24, 114, 144. Thedata processor 192 may further execute a web-based application 198 forreceiving and formatting BG data transmitted from one or more of thepatient's devices 110 a, 110 b, 124, 123 a, 123 b and storing the BGdata in non-transitory memory 24, 114, 144.

The service provider 130 may include a data processor 132 incommunication with non-transitory memory 134. The service provider 130provides the patient 10 with a process 200 (see FIG. 2A) (e.g., a mobileapplication, a web-site application, or a downloadable program thatincludes a set of instructions) executable on a processor 112, 132, 142,192 of the dosing controller 160 and accessible through the network 20via the patient device 110, health care provider electronic medicalrecord systems 140, portable blood glucose measurement devices 124(e.g., glucose meter or glucometer), or portable administration devices123 a, 123 b.

In some implementations, a health care provider medical record system140 is located at a clinic 42 (or a doctor's office) and includes a dataprocessor 142, a non-transitory memory 144, and a display 146 (e.g.,touch display or non-touch display). The transitory memory 144 and thedisplay 146 are in communication with the data processor 142. In someexamples, the health care provider electronic medical system 140includes a keyboard 148 in communication with the data processor 142 toallow a user 40 to input data, such as patient-state information 208 a(FIGS. 2A-2C). The non-transitory memory 144 maintains patient recordscapable of being retrieved, viewed, and, in some examples, modified andupdated by authorized hospital personal on the display 146.

The dosing controller 160 is in communication with the glucometer 124,insulin administration device 123 a, 123 b and includes a computingdevice 112, 132, 142 and non-transitory memory 114, 134, 144 incommunication with the computing device 112, 132, 142. The dosingcontroller 160 executes the process 200. The dosing controller 160stores patient related information retrieved from the glucometer 124 todetermine an insulin dose rate IRR based on the received blood glucosemeasurement BG. The dosing controller 160 may store blood glucose BGhistory data 208 b associated with the patient 10 that includestreatment doses and outcome attributes associated with each treatmentdose administered by the patient. For example, the treatment dose mayinclude the insulin dose rate IRR administered to the patient 10 and theoutcome attribute may include the BGnext associated with theadministered IRR. In other examples, the treatment dose may include oneor more ADMs administered to the patient 10 and the outcome attributemay include a next scheduled A1c level associated with the administeredADMs.

Referring to FIG. 1C, in some implementations, the insulin device 123(e.g., administration device), in communication with the dosingcontroller 160, is capable of executing instructions for administeringinsulin according to a subcutaneous insulin treatment program selectedby the dosing controller 160. The administration device 123 may includethe insulin pump 123 a or the pen 123 b. The administration device 123is in communication with the glucometer 124 and includes a computingdevice 112 d, 112 e and non-transitory memory 114 d, 114 e incommunication with the computing device 112 d, 112 e. The administrationdevice 123 includes a doser 223 a, 223 b in communication with theadministration computing device 112 d, 112 e for administering insulinto the patient 10. For instance, the doser 223 a of the insulin pump 123a includes an infusion set including a tube in fluid communication withan insulin reservoir and a cannula inserted into the patient's 10 bodyand secured via an adhesive patch. The doser 223 b of the pen 123 bincludes a needle for insertion into the patients 10 for administeringinsulin from an insulin cartridge. The administration device 123 mayreceive a subcutaneous insulin treatment program selected by andtransmitted from the dosing controller 160, while the administrationcomputing device 112 d, 112 e may execute the subcutaneous insulintreatment program. In some examples, the dosing controller 160 executeson the administration computing device 112 d, 112 e. Executing thesubcutaneous insulin treatment program by the administration computingdevice 112 d, 112 e causes the doser 223 a, 223 b to administer doses ofinsulin specified by the subcutaneous insulin treatment program. Forinstance, units for the doses of insulin may be automatically set ordialed in by the administration device 123 a, 123 b and administered viathe doser 223 a, 223 b to the patient 10. Accordingly, theadministration devices 123 a, 123 b may be “smart” administrationdevices capable of communicating with the dosing controller 160, orimplementing the dosing controller 160, to populate recommended doses ofinsulin for administering to the patient 10.

The network 20 may include any type of network that allows sending andreceiving communication signals, such as a wireless telecommunicationnetwork, a cellular telephone network, a time division multiple access(TDMA) network, a code division multiple access (CDMA) network, Globalsystem for mobile communications (GSM), a third generation (3G) network,fourth generation (4G) network, a satellite communications network, andother communication networks. The network 20 may include one or more ofa Wide Area Network (WAN), a Local Area Network (LAN), and a PersonalArea Network (PAN). In some examples, the network 20 includes acombination of data networks, telecommunication networks, and acombination of data and telecommunication networks. The patient device110, the service provider 130, and the hospital electronic medicalrecord system 140 communicate with each other by sending and receivingsignals (wired or wireless) via the network 20. In some examples, thenetwork 20 provides access to cloud computing resources, which may beelastic/on-demand computing and/or storage resources 24 available overthe network 20. The term ‘cloud’ services generally refers to a serviceperformed not locally on a user's device, but rather delivered from oneor more remote devices accessible via one or more networks 20.

Referring to FIGS. 1B and 2A-2D, the process 200 receives parameters(e.g., patient condition parameters) inputted via the client device 110,the service provider 130, and/or the clinic system 140, analyzes theinputted parameters and starts a patient treatment program 300 b (FIG.3B). Moreover, FIG. 2D shows a start mode selector 301 for starting atraining program 300 a that may be run at calendar intervals. Thetraining program 300 a enables the program to train itself to performmore effectively. To accomplish this, it retrieves a set of new trainingdata 308 from the BG history data 208 b and processes it to learn anup-to-date predictive model capable of predicting glycemic variables fora wide number of patient-profiles. From the training data 308, thetraining program 300 a uses patient-state information 208 a, BG historydata 208 b, and/or SubQ information 208 c to tabulate counts andcalculate probabilities, averages, regression functions, and/or otherstatistical data that may be saved (in the non-transitory memory 114,134, 144). After the training run is complete, the probabilities andother statistical data are available for use by the treatment program300 b to predict an optimum treatment dose for the patient 10 that willyield a favorable outcome attribute. For example, the treatment program300 b may adjust a recommended dose of insulin for a SubQ meal bolusadjustment program 400 a (FIG. 4A) or a SubQ basal adjustment program400 b (FIG. 4B) to bring and maintain a patient's blood glucose level BGas close to a target center BG_(TC) of a preferred target range BG_(TR).

In some implementations, before the process 200 begins to receive theparameters, the process 200 may receive a username and a password (e.g.,at a login screen displayed on the display 116, 146) to verify that aqualified and trained healthcare professional 40 is initiating theprocess 200 and entering the correct information that the process 200needs to accurately administer insulin to the patient 10. The system 100may customize the login screen to allow a user 40 to reset theirpassword and/or username. Moreover, the system 100 may provide a logoutbutton (not shown) that allows the user 40 to log out of the system 100.The logout button may be displayed on the display 116, 146 at any timeduring the execution of the process 200.

The clinical decision support system 100 may include an alarm system 120that alerts a user 40 when the patient's blood glucose level BG isoutside the target range BG_(TR). The alarm system 120 may produce anaudible sound via speaker 122 in the form of a beep or some like audiosounding mechanism. In some examples, the alarm system 120 displays awarning message or other type of indication on the display 116 a-e ofthe patient device 110 to provide a warning message. The alarm system120 may also send the audible and/or visual notification via the network20 to the clinic system 140 (or any other remote station) for display onthe display 146 of the clinic system 140 or played through speakers 152of the clinic system 140.

For commencing the training program 300 a or the patient treatmentprogram 300 b, the process prompts a user 40 to input patientinformation 208 a-c at block 208. The user 40 may input the patientinformation 208 a-c, for example, via the user device 110 or via thehealth care provider medical record system 140 located at a clinic 42(or a doctor's office). The user 40 may input new patient information208 a-c as shown in FIG. 2B. The process 200 may retrieve the patientinformation 208 a-c from the non-transitory memory 144 of the clinic'selectronic medical system 140 or the non-transitory memory 114 of thepatient device 110 (e.g., where the patient information 208 a-c waspreviously entered and stored). The patient information 208 a-c mayinclude, but is not limited to, patient-state information 208 a, BGhistory data 208 b, and SubQ information 208 c associated with thepatient 10.

Referring to FIGS. 2A and 2C, the patient-state information 208 a forthe patient 10 may include one or more patient-state attributesassociated with the patient 10. The patient-state attributes areassociated with attributes that do not change, change slowly, or changeinfrequently. For example, the patient-state information 208 a mayinclude, but is not limited to a patient's name, a patient'sidentification number (ID), a patient's height, weight, date of birth,diabetes history, disease history, clinical attributes, financialattributes, and any other relevant information. The disease history mayinclude a list of all diseases of the patient 10 and a list of allmedications prescribed for treating those diseases. Information in thedisease history may indicate whether the patient 10 has importantcomorbidities that may dictate the personalized diabetes treatmenttherapy prescribed to the patient 10. The clinical attributes mayinclude all of the patient's medical providers and a record of pertinentinformation relating to past clinical visits. For instance, the clinicalattributes may include symptoms and/or test results associated with thepatient 10. Here, symptoms and/or test results may indicate whether ornot the patient 10 has established vascular complications. Financialattributes may include insurance coverage, salary, and/or education ofthe patient 10 for considering when prescribing a particular medication.For example, medications that are not covered by a patient's insuranceplan may be difficult prescriptions for the patient to sustain, andtherefore, alternative medications may need to be considered. The otherrelevant patient-state information may include, but is not limited to, alife expectancy of the patient, important comorbidities, establishedvascular complications, whether the patient's resources and supportsystem are readily available or limited, and patient attitude. Forexample, the patient attitude may indicate whether the patient 10 ishighly motivated and adherent with excellent self-care capacities, orwhether the patient 10 is less motivated and non-adherent with poorself-care capabilities.

The BG history data 208 b includes treatment doses and outcomeattributes associated with each treatment dose administered by thepatient. FIG. 2B shows the display 116, 146 prompting the user 40 withthe option to manually input the BG history data 208 b of the patient 10upon selection of a “Manual” button or to download the BG history data208 b upon selection of a “Download” button. For instance, the patient's10 smartphone 110 b or tablet may communicate with the glucometer 124and/or the insulin administration devices 123 a, 123 b via Bluetooth orother connection to download the BG history data 208 b from the memory114 c of the glucometer 124, and transmit the downloaded BG history data208 b to the dosing controller 160 through the network 20. In otherexamples, the glucometer 124 may communicate directly with the dosingcontroller 160 to transmit the BG history data 208 b from the memory 114c of the glucometer 124 to the dosing controller 160 through the network20. FIG. 2C shows the display 116, 146 displaying the BG history data208 b as a chronological record of each insulin dose administered to thepatient 10 and the outcome history indicating the BGnext occurring aftereach insulin dose. For example, the BGnext may correspond to a nextscheduled BG measurement that occurs at a meal time after administeringa meal bolus for a previous meal. In some examples, the BGnextassociated with a breakfast bolus occurs at a pre-lunch time, the BGnextassociated with a lunch bolus occurs at a pre-dinner time, and the BGnext associated with a dinner bolus occurs at bedtime or at the nextday's pre-breakfast time.

In some examples, the process 200 at block 208 requests the user 40 toenter SubQ information 208 c for the patient 10, such as patientdiabetes status, subcutaneous type ordered for the patient 10 (e.g.,Basal/bolus and correction that is intended for patients on a consistentcarbohydrate diet, total daily dosage (TDD), bolus insulin type (e.g.,Novolog), basil insulin type (e.g., Lantus) and frequency ofdistribution (e.g., 1 dose per day, 2 doses per day, 3 doses per day,etc.), basil time, basal percentage of TDD, meal bolus percentage ofTDD, daily meal bolus distribution (e.g., breakfast bolus, lunch bolusand dinner bolus), or any other relevant information. In someimplementations, TDD is calculated in accordance with equation:TDD=QuickTransitionConstant*M _(Trans)  (4A)where QuickTransitionConstant is usually equal to 1000, and M_(Trans) isthe patient's multiplier at the time of initiation of the SubQtransition process. In other implementations, the TDD is calculated by astatistical correlation of TDD as a function of body weight. Thefollowing equation is the correlation used:TDD=0.5*Weight (kg)  (4B)In other implementations, the patient's total daily dose TDD iscalculated in accordance with the following equation:TDD=(BG_(Target) −K)*(M _(Trans))*24  (4C)where M_(Trans) is the patient's multiplier at the time of initiation ofa SubQ transition process.

In some implementations, the patient SubQ information 208 c isprepopulated with default parameters, which may be adjusted or modified.In some examples, portions of the patient SubQ information 208 c areprepopulated with previously entered patient subcutaneous information208 c. The process 200 may prompt the request to the user 40 to enterthe SubQ information 208 c on the display 116 of the patient device 110.In some implementations, the process 200 prompts the request on thedisplay 116 for a custom start of new patients (FIG. 2B) undertaking thetraining program 300 a or the treatment program 300 b. The user 40 mayenter SubQ information 208 c including the patient's 10 correctionfactor CF (e.g., 1700) and target BG range for calculating thecorrection bolus CB using EQ. 2. As shown in FIG. 2B, the user 40 mayenter an Insulin-to-Carbohydrate Ratio (ICR) for determining arecommended insulin dose based on a number of carbohydrates that thepatient 10 consumes at an associated meal.

In some examples, the display 116, 146 may show the patient-stateinformation 208 a, the BG history data 208 b, and/or the SubQinformation 208 c for an existing or returning patient 10. In thesescenarios, the patient information 208 a-c may be retrieved from thenon-transitory memory 24, 114, 124 of the system 100 and displayed uponthe display 116, 146 by entering the patient's 10 name and/oridentification ID number (FIG. 2C). Once the process 200 obtains all thepatient information 208 a-c, the process 200 allows the user 40 to startthe patient treatment program 300 b. For example, FIGS. 2B and 2C allowthe user 40 to start the patient treatment program 300 b by selecting a“Treatment” button upon the display 116, 146.

The process 200 may allow the user 40 to determine a frequency and/ordate-range of training data for use by the training program 300 a (FIG.3A). FIG. 2D shows an exemplary start mode selector 301 for the trainingprogram 300 a that allows the user 40 to select one of an automaticstart 303 of the training program 300 a, an immediate start 305 of thetraining program 300 a, or a clean re-start 307 of the training program300 a. The automatic start 303 may be a default setting for the trainingprogram 300 a and may be configurable at an interval of Ndays. In someexamples, the value for Ndays is equal to 60 days. However, the user 40may desire an early or un-scheduled start of the training program 300 aby selecting the immediate start 305. In some scenarios, selection ofthe clean re-start 307 re-initializes self-learning memories of thetraining program 300 a (e.g., stored in non-transitory memory 24, 114,124). The user 40 may select the clean re-start when changes to thetraining program 300 a have been implemented or changes will occur at aknown date. Accordingly, the user 40 may select a start of the datadate-range via a calendar pull-down button, as shown on the display 116,146 of FIG. 2D.

Referring to FIG. 3A, in some implementations, the training program 300a commences based on the selected user input to the start mode selector301 of FIG. 2D. For instance, the training program 300 a may start atblock 302 in response to a selection of one of the automatic start 303,the immediate start 305, or the clean re-start 307 at the start modeselector 301 of FIG. 2D. The clean re-start 307 may be accompanied by acustom input start date and used when changes to the training program300 a result in previously obtained data to now be obsolete. FIG. 3Ashows an overview of the training program 300 a operating in theautomatic start 303 with a time period of Ndays (e.g., 60 days). Aprocess timer 306 may initiate to count the time period. The trainingprogram 300 a is a periodic optimization process that may improve aninsulin dose-advising program for treating diabetic patients. Thetraining program 300 a may apply to a SubQ meal bolus adjustment program400 a (FIG. 4A) and a SubQ basal adjustment program 400 b (FIG. 4B).Each of the programs 400 a, 400 b may include decision trees foradjusting recommended insulin doses for meal bolus or basal,respectively.

At block 308, the training program 300 a obtains the BG history data 208b associated with the patient 10 and provides the BG history data 208 bas new training data at block 310. The new training data at block 310also includes the patient's patient-state information 208 a and thepatient's SubQ information 208 c. In some examples, only the BG historydata 208 b since the last cycle of the training program 300 a isconsidered for use in the instant training program 300 a. The new datamay be added by default to the old data already stored in the trainingprogram 300 a memory (non-transitory memory 24, 114, 124). If changeshave been made to the training program 300 a, however, the user 40 mayoverride the default and direct a re-initialization of the memories inthe training program 300 a via selection of the clean re-start 307 inthe start mode selector 301 of FIG. 2D.

The training program processes the new training data at block 310 toobtain counts and calculate probabilities in the manner of tree-trainingbased on the patient's patient-state information 208 a, BG history data208 b, and/or SubQ information 208 c. The training program 300 a mayobtain the new training data at block 310 chronologically one doseadjustment (e.g., bolus or basal) at a time. The BG history data 208 bmay include a calculation of an insulin dose administered to the patient10 and the outcome data associated with the insulin dose, obtained at atime when the next scheduled blood glucose measurement BGnext shows theresult of the administered insulin dose.

At block 312, each patient-state attribute PA, PA1-PAj of the patient'spatient-state information 208 a is provided to block 314. A treeconnector for each patient-state attribute PA may extend from block 312for input in block 314. In some examples, block 314 corresponds to aparent box associated with all the patient-state attributes PA of thepatient 10 and collectively includes one or more child boxes eachpertaining to respective ones of the patient-state attributes PA. Thetraining program 300 a, at block 314, may increment counts for eachpatient-state attribute during each dose adjustment, and calculateprobabilities for each patient-state attribute. Accordingly, thetraining program 300 a may designate an appropriate box for eachpatient-state attribute of the patient 10 one dose adjustment at a time.Advantageously, the training program 300 a builds a classifier for apatient population that may be used to predict group attributes of newcases from the domain knowledge base based on values of otherattributes. While FIG. 3A shows the training program 300 a using thedecision tree for classifying attributes, the training program 300 a mayalso use other classification methods such as IF-Then Rule Induction,Bayesian Classifiers, Naïve Baysian Classifiers, Iterative Dichotomiser3 (ID3) algorithms, K Nearest Neighbor, and Neural Networks.

The training program 300 a, at block 316, processes the patient-stateinformation 208 a, the BG history data 208 b, and the SubQ information208 c to calculate the adjusted insulin dose for the patient 10, andsubsequently recommend the adjusted insulin dose to the patient 10. Forexample, the dosing controller 160 may transmit the adjusted insulindose to the patient computing device 110. In some examples, the dosingcontroller 160 transmits the adjusted insulin dose calculated at block316 to the administration device 123 a, 123 b and the doser 223 a, 223 badministers the insulin to the patient 10. Block 316 may include eachadjusted insulin dose TA, TA1-TAj during the time period of Ndaysselected for the training program. The training program 300 a, at block316, may increment counts for each adjusted insulin dose, and calculateprobabilities for each of the adjusted insulin doses.

Referring to block 318, the training program 300 a obtains an outcomeattribute associated with the adjusted insulin dose calculated andadministered at block 316. Block 318 may include outcome attributes OA,OA1-OAi associated with each of the adjusted insulin doses TA, TA1-TAjadministered by the patient 10 at block 316. The training program 300 a,at block 318, may increment counts for each outcome attribute, andcalculate probabilities for each of the outcome attributes.

The outcome attribute may include the BGnext occurring a sufficientperiod of time (e.g., four to six hours) after the adjusted dose isadministered by the patient. In some examples, when the adjusted dosecorresponds to a meal bolus dose, the training program 300 a calculatesan evaluation or a “grade” on the adjusted dose administered by thepatient 10 at block 316. For example, the BGnext may be furtherprocessed into another outcome attribute, BG Percent Error (Err %),calculated in accordance with the following equation:Err %=ABS[(BGnext−BG_(TC))/BG_(TC)]  (5)where BG_(TC) is the Target Center of the BG Target Range BG_(TR)obtained from the SubQ information 208 c.

Subsequently, at block 318, the training program 300 a may average theBGnext (MeanBGnext) and the Err % (MeanErr %) for each adjusted insulindose and store the values in a child box contained by a parent boxcorresponding to the adjusted meal bolus dose. Accordingly, the trainingprogram 300 a calculates the evaluation or “grade” on each adjusted mealbolus dose based on one or more outcome attributes associated with theadjusted meal bolus dose. Here, adjusted meal bolus doses of insulinresulting in favorable outcome attributes are assigned higher “grades”than those resulting in less favorable outcome attributes. Thus, thetraining program 300 a may identify a best or optimum treatment dose ofinsulin (e.g., best or optimum meal bolus dose) from one of the adjustedmeal bolus doses of insulin administered by the patient that yields themost favorable outcome attribute.

In some examples, when the adjusted insulin dose calculated andadministered by the patient 10 at block 316 corresponds to an adjustedbasal dose, the outcome attribute at block 318 may include a nextscheduled breakfast BG measurement (BGbreakfastNext) occurring after theadjusted basal dose is administered at block 316. Here, theBGbreakfastNext is obtained after the patient 10 has fasted during sleepto accurately show how well the adjusted basal dose controlled thepatient's blood glucose levels. In some examples, when the adjusted dosecorresponds to the basal dose, the training program 300 a calculates anevaluation or a “grade” on the adjusted dose administered by the patient10 at block 316. For example, the BGbreakfastNext may be furtherprocessed into another outcome attribute, BGbreakfast Percent Error(BrkErr %), calculated in accordance with the following equation:BrkErr %=ABS[(BGBreakfastNext−BG_(TC))/BG_(TC)]  (6)

Subsequently, at block 318, the training program 300 a may average theBrkErr % (MeanBrkErr %) for each adjusted insulin dose and store thevalues in a child box contained by a parent box associated with theadjusted dose corresponding to the adjusted basal dose. Accordingly, thetraining program 300 a calculates the evaluation or “grade” on eachadjusted basal dose based on one or more outcome attributes associatedwith the adjusted basal dose. Here, adjusted basal doses of insulinresulting in favorable outcome attributes are assigned higher “grades”than those resulting in less favorable outcome attributes. Thus, thetraining program 300 a may identify a best or optimum treatment dose ofinsulin (e.g., best or optimum basal dose) from one of the adjustedbasal doses of insulin administered by the patient that yields the mostfavorable outcome attribute.

The training program 300 a may proceed back to block 302 and obtain theBG history data 208 b at block 308 for another dose-adjustment history.Upon completing the training program 300 a for each insulin dose to beadjusted, the training program 300 a stops the iterative process ofincrementing the counts and calculating the probabilities within thenon-transitory memory 24, 114, 124 at each of blocks 314, 316, 318. Thetraining program 300 a may build the decision tree for a patientpopulation based on the patient-state information 208 a, BG history data208 b, and the SubQ information 208 c for each patient of the patientpopulation. Accordingly, the decision tree may be retrieved from thenon-transitory memory 24, 114, 124 for use by the patient treatmentprogram 300 b to allow the user 40 (e.g., physician or medicalprofessional) to prescribe a personalized patient treatment therapy fora patient 10.

Referring to FIG. 3B, in some implementations, the treatment program 300b uses the training data contained in the decision tree processed by thetraining program 300 a for determining an optimum insulin dose andrecommending the optimum insulin dose to a patient in treatment. Thetreatment program 300 b commences at block 330 with a new patient or areturning patient. At block 330, the treatment program obtains thepatient information associated with the new or returning patient. Thepatient information may include patient-state information 208 a, BGhistory data 208 b, and SubQ information 208 c.

At block 312, each patient-state attribute PA, PA1-PAj of the patient'spatient-state information 208 a is provided to block 314. A treeconnector for each patient-state attribute PA may extend from block 312for input in block 314. In some examples, block 314 corresponds to aparent box associated with all the patient-state attributes PA of thepatient 10 and collectively includes one or more child boxes eachpertaining to respective ones of the patient-state attributes PA.Accordingly, the treatment program 300 b may assign each patient-stateattribute of the patient 10 into a corresponding box associated with thetraining program 300 a of FIG. 3A. The treatment program 300 b, however,does not increment counts or calculate probabilities for thepatient-state attributes associated with the patient 10 being treated.

At block 336, the treatment program 300 b processes the patient-stateinformation 208 a, the BG history data 208 b, and the SubQ information208 c to determine a recommended insulin dose RA1, RA2-RAj for thepatient 10. However, rather than providing the recommended insulin dosefor the patient 10 to administer, the treatment program 300 b, at block338, compares the recommended insulin dose with the adjusted insulindoses TA, TA1-TAj and the associated outcome attributes OA, OA1-OAiobtained from the training program 300 a. Here, the adjusted insulindoses TA, TA1-TAj calculated in block 316 of the training program 300 aand the associated outcome attributes OA, OA1-OAi obtained from blocks318 and 338, respectively, of the training program 300 a, may onlycorrespond to patients of the patient population that have the samediagnosis and similar patient-state attributes as the patient 10 beingtreated. Accordingly, the treatment program 300 b, at block 340, maydetermine (e.g., predict) an optimum insulin dose for the patient 10that will yield a most favorable outcome attribute based on thecomparison with the adjusted insulin doses and associated outcomeattributes obtained from the training program 300 a. In some examples,the optimum insulin dose for the patient 10 corresponds to the besttreatment dose of insulin identified at block 318 of the patienttraining program 300 a for one or more patients of the patientpopulation that have similar patient-state attributes as the patient 10being treated. Block 340 may provide the optimum insulin dose for thepatient 10 to block 336 to replace the insulin dose previouslyrecommended. The reporting module 80 of the system 100 may recommend theoptimum insulin to the patient 10 by transmitting the optimum insulindose to the patient computing device 110 for the patient 10 to view uponthe display 116, transmitting an email containing the optimum insulindose to the patient 10, and/or printing the optimum insulin dose in areport for the patient 10. In some examples, the dosing controller 160executing the treatment program 300 b may transmit the optimum insulindose to the administration device 123 (e.g., pump 123 a or smart pen 123b) so that the administration computing device 112 d, 112 e may instructthe doser 223 a, 223 b to administer the optimum insulin dose to thepatient 10.

Referring to FIG. 3C, in some implementations, an anti-diabetes program300 c prescribes ADM dose-combinations to a patient 10 for delaying, andin some instances eliminating, the progression of diabetes. FIG. 3Cprovides an overview combining the training and treatment programs 300a, 300 b, respectively, for adjusting ADMs. Accordingly, the presentdisclosure may refer to the program 300 c as an anti-diabetes program oran ADM program. The program 300 c may operate in a training mode or in atreatment mode. The training mode of the program 300 c includes aperiodic optimization process that may improve a non-insulindose-advising program for treating patients with a high probability ofbecoming diabetic or for treating patients diagnosed with Type 2diabetes mellitus (DM2). The anti-diabetes program may apply to an ADMadjustment program 400 c (FIG. 4C) that may include decision tress foradjusting recommended doses of one or more ADMs. The training mode ofthe anti-diabetes program 300 c may start in response to a selection ofone of the automatic start 303, the immediate start 305, or the cleanre-start 307 at the start mode selector 301 of FIG. 2D. The cleanre-start 307 may be accompanied by a custom input start date and usedwhen changes to the anti-diabetes program 300 c result in previouslyobtained data to now be obsolete. FIG. 3C shows the anti-diabetesprogram 300 c operating in the automatic start 303 with a time period ofNdays (e.g., 60 days). A process timer 356 may initiate to count thetime period.

At block 358, the training mode of the program 300 c obtains the BGhistory data 208 b associated with the patient 10 and provides the BGhistory data 208 b as new training data at block 360. The new trainingdata at block 360 may also include the patient's patient-stateinformation 208 a. In some examples, only the BG history data 208 bsince the last cycle of the training mode of the program 300 a isconsidered for use in the instant training mode cycle. The new data maybe added by default to the old data already stored in the trainingprogram 300 a memory (non-transitory memory 24, 114, 124). If changeshave been made to the anti-diabetes program 300 c, however, the user 40may override the default and direct a re-initialization of the memoriesin the program 300 c via selection of the clean re-start 307 in thestart mode selector 301 of FIG. 2D.

During the training mode, the anti-diabetes program 300 c processes thenew training data at block 360 to obtain counts and calculateprobabilities in the manner of tree-training based on the patient'spatient-state information 208 a and the BG history data 208 b. Thetraining mode of the program 300 c may obtain the new training data atblock 360 chronologically one ADM dose adjustment at a time. The BGhistory data 208 b may include a calculation of one or more ADMdose-combinations administered by the patient 10 and the outcome datacorresponding to an A1c level associated with the one or more ADM dosesadministered by the patient 10. Additionally or alternatively, theoutcome data may include a next scheduled blood glucose measurementBGnext occurring a sufficient time after the patient 10 administers theADM dose-combination, and thereby showing the glycemic result of the ADMdose-combination.

At block 362, each patient-state attribute PA, PA1-PAj of the patient'spatient-state information 208 a is provided to block 364. A treeconnector for each patient-state attribute PA may extend from block 362for input in block 364. In some examples, block 364 corresponds to aparent box associated with all the patient-state attributes PA of thepatient 10 and collectively includes one or more child boxes eachpertaining to respective ones of the patient-state attributes PA. Duringthe training mode, the anti-diabetes program 300 c, at block 364, mayincrement counts for each patient-state attribute during each doseadjustment, and calculate probabilities for each patient-stateattribute. Accordingly, the training mode of the program 300 c maydesignate an appropriate box for each patient-state attribute of thepatient 10 one dose adjustment at a time. Advantageously, the trainingmode of the program 300 c builds a classifier for a patient populationthat may be used to predict group attributes of new cases from thedomain knowledge base based on values of other attributes. While FIG. 3Cshows the anti-diabetes program 300 c using the decision tree forclassifying attributes, the anti-diabetes program 300 c may also useother classification methods such as IF-Then Rule Induction, BayesianClassifiers, Naïve Baysian Classifiers, Iterative Dichotomiser 3 (ID3)algorithms, K Nearest Neighbor, and Neural Networks.

The anti-diabetes program 300 c, at block 366, processes thepatient-state information 208 a, the BG history data 208 b, and the SubQinformation 208 c to calculate the adjusted ADM dose-combination for thepatient 10 and subsequently recommends the adjusted ADM dose-combinationto the patient 10. Block 366 may include each ADM dose-combination TA,TA1-TAj and corresponding ADMs ADM1, ADM2-ADMk during the time period ofNdays selected for the training mode of the anti-diabetes program 300 c.The anti-diabetes program 300 c, at block 368, may increment counts foreach ADM dose-combination, and calculate probabilities for each of theADM dose-combinations during the training mode. During the treatmentmode, however, block 366 does not increment counts or calculateprobabilities.

Referring to block 368, the program 300 c obtains an outcome attributeassociated with the ADM dose-combinations calculated and administered atblock 366. Block 368 may include outcome attributes OA, OA1-OAiassociated with each of the ADM dose-combination TA, TA1-TAjadministered by the patient 10 at block 366. The training mode of theanti-diabetes program 300 c, at block 368, may increment counts for eachoutcome attribute, and calculate probabilities for each of the outcomeattributes. During the treatment mode, however, the anti-diabetesprogram 300 c does not increment counts or calculate probabilities atblock 368.

The outcome attribute may include a next scheduled A1c level (A1cNext)occurring after the ADM dose-combination is administered by the patient.In some scenarios, the A1cNext is obtained by the anti-diabetes program300 c within two to three months after the ADM dose-combination isadministered by the patient 10. In some examples, during the trainingmode, the anti-diabetes program 300 c calculates an evaluation or a“grade” on the ADM dose-combination administered by the patient 10 atblock 366. For example, the anti-diabetes program 300 c may average theA1cNext (MeanA1cNext) for each of the ADM dose-combination administeredby the patient 10 and store the values in a child box contained by aparent box associated with ADM dose-combinations.

The training mode of the anti-diabetes program 300 c may proceed back toblock 352 and obtain the BG history data 208 b at block 358 for anotherdose-adjustment history. Upon adjusting each ADM dose during thedesignated period of time Ndays, the training mode ends and theanti-diabetes program 300 c stops the iterative process of incrementingthe counts and calculating the probabilities within the non-transitorymemory 24, 114, 124 at each of blocks 364, 366, 368. As with thetraining program 300 a of FIG. 3A, the anti-diabetes program 300 c mayprocess a decision tree during the training mode for a patientpopulation based on the patient-state information 208 a and the BGhistory data 208 b for each patient of the patient population.

During the treatment mode, the anti-diabetes program 300 c uses thetraining data contained in the decision tree processed during thetraining mode for determining an optimum ADM dose-combination andrecommending the optimum ADM dose-combination to a patient 10 intreatment. The treatment mode may commence at block 360 by obtainingpatient-state information 208 a and BG history data 208 b associatedwith a new or returning patient 10. As with the training mode, thetreatment mode of the anti-diabetes program 300 c may assign eachpatient-state attribute of the new or returning patient 10 into acorresponding box at block 364. However, the treatment mode does notincrement counts or calculate probabilities for the patient-stateattributes associated with the patient 10 being treated. At block 366,the anti-diabetes program 300 c processes the patient-state information208 a and the BG history data 208 b and calculates a recommended ADMdose-combination for the patient 10. Rather than providing therecommended ADM dose-combination for the patient 10 to administer, thetreatment mode of the program 300 c, at block 368, compares therecommended ADM dose-combination with the adjusted ADM dose-combinationsTA, TA1-TAj and associated outcome attributes OA, OA1, OAi obtained fromthe training data during the training mode. Here, the anti-diabetesprogram 300 c may compare the recommended ADM dose-combination with theadjusted ADM dose-combinations TA, TA1-TAj and associated outcomeattributes OA, OA1, OAi that only correspond to patients of the patientpopulation that have the same diagnosis and similar patient-stateattributes as the patient 10 being treated. According, at block 370, thetreatment mode of the program 300 c may determine an optimum ADM(insulin or non-insulin) dose-combination for the patient 10 that thatwill yield a most favorable outcome attribute based on the adjusted ADMdose-combinations and associated outcome attributes obtained from thetraining data. Block 370 may provide the optimum ADM dose-combination toblock 366 to replace the ADM dose-combination previously recommended.The reporting module 80 of the system 100 may recommend the optimum ADMdose-combination to the patient 10 by transmitting the optimum ADMdose-combination to the patient computing device 110 for the patient 10to view upon the display 116, transmitting an email containing theoptimum ADM dose-combination to the patient 10, and/or printing theoptimum ADM dose-combination in a report for the patient 10.

Referring to FIG. 4A, in some implementations, a SubQ meal bolusadjustment program 400 a may operate in a training mode associated withthe training program 300 a (FIG. 3A) and a treatment mode associatedwith the treatment program 300 b (FIG. 3B). FIG. 4A provides details oftraining and treatment for adjusting meal boluses. The SubQ meal bolusadjustment program 400 a may execute on a computing device, such asservice provider data processing hardware 130, 160, a cloud resource, orsome other computing device 112, 132, 142. As with the training program300 a, the training mode of the SubQ meal bolus adjustment program 400 amay start at block 402 in response to a selection of one of theautomatic start 303, the immediate start 305, or the clean re-start 307at the start mode selector 301 of FIG. 2D. The clean re-start 307 may beaccompanied by a custom input start date and used when changes to thetraining program 300 a result in previously obtained data to now beobsolete. FIG. 4A shows the training mode of program 400 a operating inthe automatic start 303 with a time period of Ndays (e.g., 60 days). Aprocess timer 406 may initiate to count the time period. The trainingmode is a periodic optimization process that may improve the meal bolusdose recommendations determined by the SubQ meal bolus adjustmentprogram 400 a.

At block 408, the treatment mode of the SubQ meal bolus adjustmentprogram 400 a obtains patient information including the BG history data208 b associated with the patient 10 and provides the BG history data208 b as new training data at block 410. The new training data at block410 also includes the patient's patient-state information 208 a and thepatient's SubQ information 208 c. The SubQ meal bolus adjustment program400 a may obtain the new training data at block 410 chronologically onemeal bolus dose adjustment at a time. The BG history data 208 b mayinclude a calculation of an insulin dose (e.g., meal bolus) administeredby the patient 10 and the outcome data associated with the insulin dose,obtained at a time when the next scheduled blood glucose measurementBGnext shows the result of the administered insulin dose.

The SubQ meal bolus adjustment program 400 a includes blocks 410-428 forprocessing associated attributes obtained from patient's patient-stateinformation 208 a, BG history data 208 b, and/or SubQ information 208 c.Each block 410-428 may obtain a count and calculate a probability forthe associated attributed in a manner of tree-training based on thepatient's patient-state information 208 a, BG history data 208 b, and/orSubQ information 208 c obtained from the training data at block 410.Each block 410-428 may include one or more child attribute boxesassociated with a parent attribute box. For each attribute, the blocks410-428 may determine an appropriate box and increment the count (N) byone and calculate a likelihood or probability (P) in each box inaccordance with the following equation:P=(N in child box)/(N in parent box)  (7)where the child box corresponds to a current attribute.

At block 412, a patient's diagnosis attribute obtained from thepatient-state information 208 a is compared to child boxes associatedwith the diagnosis attribute. The child box associated with thepatient's diagnosis is then selected and the diagnosis count Ndiag isincremented by one and the diagnosis probability Pdiag is calculatedusing Eq. 7.

At block 414, a patient's age attribute obtained from the patient-stateinformation 208 a is compared to child boxes associated with the ageattribute. Each child box may correspond to an age in years or anassociated range of ages in years. The child box associated with thepatient's age is then selected and the age count Nage is incremented byone and the age probability Page is calculated using Eq. 7. In someexamples, the box associated with the patient's age is selected byiteratively comparing the patient's age to an upper bound of the ageattribute (AgeTop) starting from the youngest age, and using a logicphrase in accordance with the following expression: IF Age<AgeTop THENNage=Nage+1.

At block 416, a patient's diabetic type DMtype obtained from thepatient-state information 208 a is compared to child boxes associatedwith the DMtype attribute. The DMtype attribute at block 416 may includethree child boxes: one box for DM1, one box for DM2, and one box for“not recorded.” The child box associated with the patient's DMType isthen selected and the DMType count NDM1 or NDM2 is incremented by oneand the probability PDM1 or PDM2 is calculated using Eq. 7.

At block 418, the patient's body mass index (BMI) is determined by theweight and height of the patient obtained from the patient-stateinformation 208 a and compared to child boxes associated with the BMIattribute. Each child box may correspond to a BMI value or an associatedrange of BMI values. The child box associated with the patient's BMI isthen selected and the BMI count NBMI is incremented by one and the BMIprobability PBMI is calculated using Eq. 7. In some examples, the boxassociated with the patient's BMI is selected by iteratively comparingthe patient's BMI to an upper bound of the BMI attribute (BMITop)starting from the lowest BMI, and using a logic phrase in accordancewith the following expression: IF BMI<BMITop THEN NBMI=NBMI+1.

At block 420, the patient's correction factor (CF) obtained from theSubQ information 208 c is compared to child boxes associated with the CFattribute. Each child box may correspond to a CF value or an associatedrange of CF values. The child box associated with the patient's CF isthen selected and the CF count NCF is incremented by one and the CFprobability PCF is calculated using Eq. 7. In some examples, the boxassociated with the patient's CF is selected by iteratively comparingthe patient's CF to an upper bound of the CF attribute (CFTop) startingfrom the lowest CF, and using a logic phrase in accordance with thefollowing expression: IF CF<CFTop THEN NCF=NCF+1.

At block 422, the SubQ meal bolus adjustment program 400 achronologically adjusts the meal bolus insulin dose one dose adjustmentat a time. The meal bolus being adjusted is referred to as a GoverningMeal Bolus (MealBolusGov). The MealBolusGov may be obtained from the BGhistory data 208 b and compared to child boxes associated with theMealBolusGov attribute. Each child box may correspond to a MealBolusGovvalue or an associated range of MealBolusGov values. The program 400 aselects the child box associated with the patient's MealBolusGov,increments a count Nbolg by one, and calculates the MealBolusGovprobability Pbolg using Eq. 7. In some examples, the box associated withthe patient's MealBolusGov is selected by iteratively comparing thepatient's MealBolusGov to an upper bound of the MealBolusGov attribute(MbolGTop) starting from the lowest MealBolusGov, and using a logicphrase in accordance with the following expression: IFMealBolusGov<MbolGTop THEN Nbolg=Nbolg+1.

At block 424, the program 400 a obtains the patient's next scheduledblood glucose measurement (BGgov) after the patient 10 administers theMealBolusGov from the BG history data 208 b and compares the BGgov tochild boxes associated with the BGgov attribute. Each child box maycorrespond to a BGgov value or an associated range of BGgov values. Theprogram 400 a selects the child box associated with the patient's BGgov,increments a count NBG by one, and calculates the BGgov probability PBGusing Eq. 7. In some examples, the box associated with the patient'sBGgov is selected by iteratively comparing the patient's BGgov to anupper bound of the BGgov attribute (BGgovTop) starting from the lowestBGgov, and using a logic phrase in accordance with the followingexpression: IF BGgov<BGgov Top THEN NBG=NBG+1.

At block 426, the SubQ meal bolus adjustment program 400 a obtains thepatient's blood glucose target range BG_(TR) from the SubQ information208 c and compares the BG_(TR) to child boxes associated with theBG_(TR) attribute. The program 400 a selects the child box associatedwith the patient's BG_(TR), increments a count NTgt by one, andcalculates the BG_(TR) probability PTgt using Eq. 7. In some examples,the box associated with the patient's BG_(TR) is selected by iterativelycomparing the patient's BG_(TR) to an upper bound of the BG_(TR)attribute (TargetHigh) starting from the greatest BG_(TR), and using alogic phrase in accordance with the following expression: IFBG_(TR)<TargetHigh THEN NTgt=NTgt+1.

The SubQ meal bolus adjustment program 400 a, at block 428, obtains thepatient's insulin-carbohydrate ratio (ICR) from the SubQ information 208c and compares the ICR to child boxes associated with the ICR attribute.The program 400 a selects the child box associated with the patient'sICR, increments a count NICR by one, and calculates the ICR probabilityPICR using Eq. 7. In some examples, the box associated with thepatient's ICR is selected by iteratively comparing the patient's ICR toan upper bound of the ICR attribute (ICRboxTop) starting from the lowestICR, and using a logic phrase in accordance with the followingexpression: IF ICR<ICRboxTop THEN NICR=NICR+1.

After the SubQ meal bolus adjustment program 400 a processes each of thepatient's attributes within blocks 410-428 during the training mode, theprogram 400 a proceeds to block 430 and obtains an Adjusted Meal Bolus(MealBolAdj). In some examples, the MealBolAdj corresponds to the nextmeal bolus occurring after the MealBolGov of block 422. For instance, ifthe MealBolGov includes a breakfast meal bolus, the MealBolAdj willcorrespond to a lunch meal bolus on the same day. In other examples, theMealBolAdj corresponds to the same meal bolus as the MealBolGov, but onthe next day. For instance, if the MealBolGov includes a dinner mealbolus, the MealBolAdj will correspond to the dinner meal bolus occurringon the next day. During the training mode, the program 400 a comparesthe MealBolAdj to child boxes associated with the MealBolAdj attribute.Each child box may correspond to a MealBolAdj value or an associatedrange of MealBolAdj values. The program 400 a selects the child boxassociated with the patient's MealBolAdj, increments a count NMbolAdj byone, and calculates the MealBolAdj probability PMBolAdj using Eq. 7. Insome examples, the box associated with the patient's MealBolAdj isselected by iteratively comparing the patient's MealBolAdj to an upperbound of the MealBolAdj attribute (MbolAdjTop) starting from the lowestMealBolAdj, and using a logic phrase in accordance with the followingexpression: IF MealBolAdj<MbolAdjTop THEN NMbolAdj=NMbolAdj+1. The SubQmeal bolus adjustment program 400 a may recommend an adjusted insulindose associated with the MealBolAdj to the patient 10 for the patient 10to administer. For example, the dosing controller 160 may transmit theMealBolAdj to the patient computing device 110 or to the patient'sadministration device 123 a, 123 b.

Subsequent to processing the patient's MealBolAdj at block 430, the SubQmeal bolus adjustment program 400 a, at block 432, obtains one or moreoutcome attributes associated with the patient's MealBolAdj. The outcomeattribute may include the BGnext that occurs a sufficient period of time(e.g., four to six hours) after the adjusted dose is administered by thepatient 10. The program 400 a may increment a count for the BGnext(NBGnext) by one. Additionally, during the training mode, the program400 a may calculate an evaluation or a “grade” on the MealBolAdjadministered by the patient 10 based on the BGnext. For example, theprogram 400 a may process the BGnext to calculate the BG Percent Error(Err %) using Eq. 5 and increment a count for the Err % (Nerr %) by one.The SubQ meal bolus adjustment program 400 a may also calculate a sumfor each BGnext (SumBGnext) and each Err % (SumErr %) for eachMealBolAdj, and then average the BGnext (MeanBGnext) and the Err %(MeanErr %) for each MealBolAdj. The values for the SumBGnext, theSumErr %, the MeanBGnext, and the MeanErr % may be stored in a child boxcontained by a parent box corresponding to the MealBolAdj. Accordingly,the meal bolus adjustment program 400 a calculates the evaluation or“grade” on each MealBolAdj based on one or more outcome attributesassociated with the MealBolAdj. Here, MealBolAdj values yieldingfavorable outcome attributes are assigned higher “grades” than thoseresulting in less favorable outcome attributes. Thus, the program 400 amay identify a best or optimum treatment dose of insulin (e.g., best oroptimum MealBolAdj) from one of the MealBolAdj doses of insulinadministered by the patient 10 that yields the most favorable outcomeattribute.

Upon completing the training mode for each MealBolAdj, the SubQ mealbolus adjustment program 400 a stops the iterative process ofincrementing the counts and calculating the probabilities within thenon-transitory memory 24, 114, 124 at each of blocks 410-430. Thus, aswith the training program 300 a (FIG. 3A), the training mode of the SubQmeal bolus adjustment program 400 a builds a decision tree for a patientpopulation based on the patient-state information 208 a, BG history data208 b, and the SubQ information 208 c for each patient of the patientpopulation. Thereafter, the treatment mode of the SubQ meal bolusadjustment program 400 a may retrieve the decision tree from thenon-transitory memory 24, 114, 124 to allow the user 40 (e.g., physicalor medical professional) to prescribe an optimum MealBolAdj that ispersonalized for a patient 10 being treated.

During the treatment mode, the SubQ meal bolus adjustment program 400 auses the training data contained in the decision tree processed duringthe training mode for determining an optimum MealBolAdj and recommendingthe optimum MealBolAdj to a patient 10 in treatment. The treatment modemay commence at block 410 by obtaining patient-state information 208 a,BG history data 208 b, and SubQ information 208 c associated with a newor returning patient 10 being treated. As with the training mode, thetreatment mode of the SubQ meal bolus adjustment process 400 a mayassign each of the new or returning patient's attributes intocorresponding boxes at each of blocks 410-428. However, the treatmentmode does not increment counts or calculate probabilities for the boxesassociated with the patient's 10 attributes. At block 430, the program400 a selects the optimum insulin dose for the MealBolAdj (e.g., optimumMealBolAdj) for the new or returning patient 10 based on the MealBolAdjfrom the training mode that is associated with a lowest MeanErr %. Thus,the treatment mode of the program 400 a selects the optimum MealBolAdjthat will yield a most favorable outcome attribute (e.g., lowest MeanErr%) based on the training data of the training mode. Block 430 may usethe reporting module 80 of the system 100 to recommend the optimumMealBolAdj to the patient 10 being treated through transmission to thepatient computing device 110 for the patient 10 to view upon thedisplay, through transmission of an email containing the optimumMealBolAdj, and/or printing the optimum MealBolAdj in a report for thepatient 10. In some examples, the dosing controller 160 executing theSubQ meal bolus adjustment program 400 a at block 430 may transmit theoptimum MealBolAdj to the administration device 123 (e.g., pump 123 a orsmart pen 123 b) so that the administration computing device 112 d, 112e may instruct the doser 223 a, 223 b to administer the optimum insulindose to the patient 10.

Referring to FIG. 4B, in some implementations, a SubQ basal adjustmentprogram 400 b may operate in a training mode associated with thetraining program 300 a (FIG. 3A) and a treatment mode associated withthe treatment program 300 b (FIG. 3B). The training mode may commenceafter the training mode of the SubQ meal bolus adjustment program 400 a(FIG. 4A) completes at block 434 of FIG. 4A. The SubQ basal adjustmentprogram 400 b uses the new training data (e.g., patient-stateinformation 208 a, BG history data 208 b, and SubQ information 208 c)from block 410 of the SubQ meal bolus adjustment program 400 a which issorted by patient ID and chronologically one basal dose adjustment at atime. Accordingly, the training mode of the SubQ basal adjustmentprogram 400 b completes the process of obtaining the BG history data 208b associated with basal insulin dose adjustments and outcome attributesassociated therewith. Thus, each basal insulin dose adjustment and theassociated one or more outcome attributes may include a respectivedate-stamped event.

The training mode of the SubQ basal adjustment program 400 b includesblocks 442-456 for processing associated attributes obtained frompatient's patient-state information 208 a, BG history data 208 b, and/orSubQ information 208 c. Similar to the training mode of the SubQ mealbolus adjustment program 400 a (FIG. 4A), each block 442-456 of the SubQbasal adjustment program 400 b during the training mode may obtain acount and calculate a probability for the associated attributed in amanner of tree-training based on the patient's patient-state information208 a, BG history data 208 b, and/or SubQ information 208 c obtainedfrom the training data at block 410. Each block 442-456 may include oneor more child attribute boxes associated with a parent attribute box.For each attribute, the blocks 442-456 may determine an appropriate boxand increment the count (N) by one and calculate a likelihood orprobability (P) in each box using Eq. 7.

At block 442-448, the training mode of the SubQ basal adjustment program400 b selects the child box, increments the count N by one andcalculates the probability P within each box for each of the attributesassociated with the patient's diagnosis, age, diabetic type DMtype, andbody mass index (BMI) in the same manner as discussed above withreference to blocks 412-418 of the SubQ meal bolus adjustment program400 a of FIG. 4A. At block 450, the training mode of the SubQ basaladjustment program 400 b obtains the patient's blood glucose targetrange BG_(TR) from the SubQ information 208 c and compares the patient'sBG_(TR) to child boxes associated with the BG_(TR) attribute at block450. Similar to block 426 of the SubQ meal bolus adjustment program 400a (FIG. 4A), the training mode of the SubQ basal adjustment program 400b selects the child box associated with the patient's BG_(TR),increments the BG_(TR) count NTgr by one, and calculates the BG_(TR)probability PTgr using Eq. 7.

At block 452, the training mode of the SubQ basal adjustment program 400b chronologically adjusts the basal insulin dose one dose adjustment ata time. The basal dose being adjusted is referred to as a GoverningBasal dose (BasalGov). The program 400 b may obtain the patient'sBasalGov from the BG history data 208 b and compare the patient'sBasalGov to child boxes associated with the BasalGov attribute. Eachchild box may correspond to a BasalGov value or an associated range ofBasalGov values. The program 400 b selects the child box associated withthe patient's BasalGov, increments a count NBasalG, and calculates aBasalGov probability PBasalG using Eq. 7. In some examples, the boxassociated with the patient's BasalGov is selected by iterativelycomparing the patient's BasalGov to an upper bound of the BasalGovattribute (BasalGTop) starting from the lowest BasalGov, and using alogic phrase in accordance with the following expression: IFBasalGov<BasalGTop THEN NBasalG=NBasalG+1.

At block 454, the training mode of the SubQ basal adjustment program 400b obtains the patient's next scheduled blood glucose measurement (BGgov)after the patient administers the BasalGov from the BG history data 208b and compares the BGgov to child boxes associated with the BGgovattribute. The BGgov associated with the patient's BasalGov occurs asufficient amount of time after the patient administers the BasalGovwhen the dose is at a full activity level when the effects of food andrapid-acting insulin are both absent, and the blood glucose level isstable. Here, the program may flag the BGgov for identification. Eachchild box may correspond to a BGgov value or an associated range ofBGgov values. The program 400 a selects the child box associated withthe patient's BGgov, increments a count NBG by one, and calculates theBGgov probability PBG using Eq. 7. In some examples, the box associatedwith the patient's BGgov is selected by iteratively comparing thepatient's BGgov to an upper bound of the BGgov attribute (BGgovTop)starting from the lowest BGgov, and using a logic phrase in accordancewith the following expression: IF BGgov<BGgov Top THEN NBG=NBG+1.

At block 456, the training mode of the SubQ basal adjustment program 400b obtains the patient's basal adjustment factor BAF from the SubQinformation 208 c and compares the patient's BAF to child boxesassociated with the BAF attribute at block 456. Each child box maycorrespond to a BAF value or an associated range of BF values. Theprogram 400 b selects the child box associated with the patient's BAF,increments a count NBAF by one, and calculates the BAF probability PBAFusing Eq. 7.

After the SubQ basal adjustment program 400 b processes each of thepatient's attributes within blocks 442-456, the program 400 b proceedsto block 458 and obtains an Adjusted Basal dose (BasalAdj). The BasalAdjcorresponds to the next scheduled basal dose occurring after theBasalGov of block 452. During the training mode, the program 400 bcompares the BasalAdj to child boxes associated with the BasalAdjattribute. Each child box may correspond to a BasalAdj value or anassociated range of BasalAdj values. The program 400 b selects the childbox associated with the patient's BasalAdj, increments a count NBasalAdjby one, and calculates the BasalAdj probability PBasalAdj using Eq. 7.In some examples, the box associated with the patient's BasalAdj isselected by iteratively comparing the patient's BasalAdj to an upperbound of the BasalAdj attribute (BasalAdjTop) starting from the lowestBasalAdj, and using a logic phrase in accordance with the followingexpression: IF BasalAdj<BasalAdjTop THEN NBasalAdj=NBasalAdj+1. The SubQbasal adjustment program 400 b may recommend an adjusted insulin doseassociated with the BasalAdj to the patient 10 for the patient 10 toadminister. For example, the dosing controller 160 may transmit theBasalAdj to the patient computing device 110 or to the patient'sadministration device 123 a, 123 b.

Subsequent to processing the patient's BasalAdj at block 458, the SubQbasal adjustment program 400 b, at block 460, obtains one or moreoutcome attributes associated with the patient's BasalAdj. The outcomeattribute may include next scheduled breakfast BG measurement(BGbreakfastNext) occurring after the adjusted basal dose isadministered by the patient at block 358. The program 400 b mayincrement a count for the BGbreakfastNext (NBGbrkNext) by one.Additionally, during the training mode, the program 400 b may calculatean evaluation or a “grade” on the BasalAdj administered by the patient10 based on the BGbreakfastNext. For example, the program 400 b mayprocess the BGbreakfastNext to calculate the BGbreakfast Percent Error(BrkErr %) using Eq. 6 and increment a count for the BrkErr % (NBrkErr%) by one. The SubQ basal adjustment program 400 b may also calculate arunning sum for each BGbreakfastNext (SumBGbrkNext) and each BrkErr %(SumBrkErr %) for each BasalAdj, and then average the BrkErr %(MeanBrkErr %) for each BasalAdj. The values for the SumBGbrkNext, theSumBrkErr %, and the MeanBrkErr % may be stored in a child box containedby a parent box corresponding to the BasalAdj. Accordingly, the basaladjustment program 400 b calculates the evaluation or “grade” on eachBasalAdj based on one or more outcome attributes associated with theBasalAdj. Here, BasalAdj values yielding favorable outcome attributesare assigned higher “grades” than those resulting in less favorableoutcome attributes. Thus, the program 400 b may identify a best oroptimum treatment dose of insulin (e.g., best or optimum BasalAdj) fromone of the BasalAdj doses of insulin administered by the patient 10 thatyields the most favorable outcome attribute.

The training mode of the SubQ basal adjustment program 400 b may proceedback to block 410 and obtain the BG history data 208 b for another basaldose-adjustment history. Upon completing the training mode for eachbasal insulin dose to be adjusted, the SubQ basal adjustment program 400b stops the iterative process of incrementing the counts and calculatingthe probabilities within the non-transitory memory 24, 114, 124 at eachof blocks 442-460. Thus, as with the training program 300 a (FIG. 3A),the training mode of the SubQ basal adjustment program 400 b builds adecision tree for a patient population based on the patient-stateinformation 208 a, BG history data 208 b, and the SubQ information 208 cfor each patient of the patient population. Thereafter, the treatmentmode of the SubQ basal adjustment program 400 b may retrieve thedecision tree from the non-transitory memory 24, 114, 124 to allow theuser 40 (e.g., physical or medical professional) to prescribe an optimumBasalAdj that is personalized for a patient 10 being treated.

During the treatment mode, the SubQ basal adjustment program 400 b usesthe training data contained in the decision tree processed during thetraining mode for determining an optimum BasalAdj and recommending theoptimum BasalAdj to a patient 10 in treatment. The treatment mode maycommence at block 410 by obtaining patient-state information 208 a, BGhistory data 208 b, and SubQ information 208 c associated with a new orreturning patient 10 being treated. As with the training mode, thetreatment mode of the SubQ basal adjustment program 400 b may assigneach of the new or returning patient's attributes into correspondingboxes at each of blocks 410-456. However, the treatment mode does notincrement counts or calculate probabilities for the boxes associatedwith the patient's 10 attributes. At block 458, the program 400 aselects the optimum insulin dose for the BasalAdj (e.g., optimumBasalAdj) for the new or returning patient 10 based on the BasalAdj fromthe training data of the training mode that is associated with a lowestMeanBrkErr %. Thus, the treatment mode of the program 400 b selects theoptimum BasalAdj that will yield a most favorable outcome attribute(e.g., lowest MeanBrkErr %) based on the training data of the trainingmode. Block 458 may use the reporting module 80 of the system 100 torecommend the optimum BasalAdj to the patient 10 being treated throughtransmission to the patient computing device 110 for the patient 10 toview upon the display, through transmission of an email containing theoptimum BasalAdj, and/or printing the optimum BasalAdj in a report forthe patient 10. In some examples, the dosing controller 160 executingthe SubQ basal adjustment program 400 b at block 458 may transmit theoptimum BasalAdj to the administration device 123 (e.g., pump 123 a orsmart pen 123 b) so that the administration computing device 112 d, 112e may instruct the doser 223 a, 223 b to administer the optimum insulindose to the patient 10.

Referring to FIG. 4C, in some implementations, the ADM adjustmentprogram 400 c may operate in training and treatment modes associatedwith the anti-diabetes program 300 c (FIG. 3C). Accordingly, FIG. 4Cprovides details of the ADM adjustment process associated with theanti-diabetes program 300 c (FIG. 3C). The ADM adjustment program 400 cmay use the new training data (e.g., patient-state information 208 a andthe BG history data 208 b) from block 360 of the ADM program 300 c ofFIG. 3C which is sorted by patient ID and chronologically one ADMdose-combination adjustment at a time. Accordingly, the training mode ofthe ADM adjustment program 400 c completes the process of obtaining theBG history data 208 b associated with ADM dose-combination adjustmentsand outcome attributes associated therewith. Thus, each ADMdose-adjustment adjustment and the associated one or more outcomeattributes may include a respective date-stamped event.

The ADM adjustment program 400 c includes blocks 472-480 for processingassociated attributes obtained from the new patient training data fromblock 322 that includes the patient's patient-state information 208 aand the BG history data 208 b. Each block 472-480 may obtain a count andcalculate a probability for the associated attribute in the manner oftree-training based on the patient's patient-state information 208 a andthe BG history data 208 b. Each block 472-480 may include one or morechild attribute boxes associated with a parent attribute box. For eachattribute, the blocks 472-480 may determine an appropriate box andincrement the count (N) by one and calculate a likelihood or probability(P) in each box using Eq. 7.

The BG history data 208 b may include a calculation of one or more ADMdose-combinations administered by the patient 10 and the outcome datacorresponding to an A1c level associated with the one or more ADMdose-combinations administered by the patient 10. Additionally oralternatively, the outcome data may include a next scheduled bloodglucose measurement BGnext occurring a sufficient time after the patient10 administers the ADM dose-combination, and thereby showing theglycemic result of the ADM dose-combination. The ADM dose-combinationrefers to a dosing therapy that includes a combination of one or morenon-insulin doses of anti-diabetes medications.

At blocks 472-480, the training mode of the ADM adjustment program 400 cselects the child box, increments the count N by one and calculates theprobability P within each box for each of the attributes associated withthe patient's diagnosis, age, diabetic type DMtype, and body mass index(BMI) in the same manner as discussed above with reference to blocks412-418 of the SubQ meal bolus adjustment program 400 a (FIG. 4A) andblocks 442-448 of the SubQ basal adjustment program 400 b (FIG. 4B).

At block 480, the training mode of the ADM adjustment program 400 cchronologically adjusts the ADM dose-combination one dose adjustment ata time. The program uses the patient's A1c level (A1cGov) to govern theadjustment of the ADM dose-combination. The program 400 c may obtain thepatient's A1cGov from the BG history data 208 b and compare thepatient's A1cGov to child boxes associated with the A1cGov attribute.Each child box may correspond to an A1cGov value or an associated rangeof A1cGov values. The program 400 c selects the child box associatedwith the patient's A1cGov, increments a count NA1cGov, and calculates aA1cGov probability PA1cGov using Eq. 7. In some examples, the boxassociated with the patient's A1cGov is selected by iterativelycomparing the patient's BasalGov to an upper bound of the A1cGovattribute (A1cTop) starting from the lowest A1cGov, and using a logicphrase in accordance with the following expression: IF A1cGov<A1cTopTHEN NA1cGov=NA1cGov+1. For example, block 480 may include child boxescorresponding to A1cTop values equal to 6.5, 7.5, 9.0, 10.0, 11.0, and20.0.

At block 482, the patient's total daily dose (TDD) of insulin is sortedinto boxes. While TDD is a SubQ parameter, the patient's TDD may beobtained from the patient-state information 208 a using anyone of Eqs.4A-4C. The program 400 c may compare the patient's TDD to child boxesassociated with the TDD attribute. Each child box may correspond to aTDD value or an associated range of TDD values. The program 400 cselects the child box associated with the patient's TDD, increments acount NTDD, and calculates a TDD probability PTDD using Eq. 7. In someexamples, the box associated with the patient's TDD is selected byiteratively comparing the patient's TDD to an upper bound of the TDDattribute (TDDTop) starting from the lowest TDD, and using a logicphrase in accordance with the following expression: IF TDD<TDDTop THENNTDD=NTDD+1.

After the ADM adjustment program 400 c processes each of the patient'sattributes within blocks 472-482, the program 400 c proceeds to one ofblocks 484, 484 a-c and obtains an adjusted ADM dose-combination. Manyanti-diabetes medications (ADMs) currently exist and the number ofavailable ADMs for treating patient's continues to increase. Moreover,some ADMs are withdrawn or fall out of favor for prescribing to patientswhen medical research discovers faults in those ADMs. Thus, the ADMsassociated with the ADM dose-combinations of blocks 484 a-c may beupdated with new ADMs and/or with previously used ADMs being withdrawn.For simplicity, the ADM dose-combinations associated with blocks 484 a-cmay include four different ADMs (e.g., ADM1, ADM2, ADM3, ADM4) that maybe used alone or in combination with one or more of the other ADMs.

At block 484 a, the ADM adjustment program 400 c obtains each of thepatient's one or more ADMs from the patient-state information 208 a andcompare the patient's ADM(s) to child boxes associated with the ADMattribute. Each child box may correspond to a specific one of the ADMs.The program 400 c selects each child box associated with the patient'sone or more ADMs, increments a count NADM1-4 for each box selected, andcalculates ADM probability PADM1-4 for each box selected using Eq. 7.

The ADM dose-combinations of block 484 a are each associated with amono-therapy including an adjusted ADM dose of only one of ADM1, ADM2,ADM3, or ADM4. The ADM dose-combinations of block 484 b are associatedwith a dual-therapy including an adjusted ADM dose-combination of anytwo of the ADMs 1-4. The ADM dose-combinations of block 484 c areassociated with a triple-therapy including an adjusted ADMdose-combination of any three of the ADMs 1-4. Therapies including ADMdose-combinations of four or more ADM may exist when more than five ADMsare available for treating patients for delaying, and in some instances,eliminating the progression of diabetes in patients. The training modeof the ADM adjustment program 400 c may also increment counts andcalculated probabilities for each of the dose-combinations of block 484b associated with the dual-therapy and each of the dose-combinations ofblock 484 c associated with the triple-therapy.

At block 486, the ADM adjustment program 400 c obtains one or moreoutcome attributes associated with each of the adjusted ADMdose-combinations of blocks 484 a-484 c. In some examples, an outcomeattribute associated with the ADM dose-combinations may include a nextA1c result (A1cNext) occurring after the associated adjusted ADMdose-combination is administered by the patient 10. Block 486 may obtainthe patient's A1cNext from the BG History data 208 b and compare thepatient's A1cNext to child boxes associated with the A1cNext attribute.The program 400 c selects the child box associated with the patient'sA1cNext and increments a count NA1cNext for the selected box by one.

The training mode of the ADM adjustment program 400 c may proceed backto block 322 and obtain the BG history data 208 b for another ADMdose-adjustment history. Upon completing the training mode for each ADMdose-combination to be adjusted, the ADM adjustment program 400 c stopsthe iterative process of incrementing the counts and calculating theprobabilities within the non-transitory memory 24, 114, 124. Thus, theADM adjustment program 400 c builds a decision tree for a patientpopulation based on the patient-state information 208 a and the BGhistory data 208 b for each patient of the patient population. In someimplementations, block 486 averages all of the obtained A1cNext values(MeanA1CNext) for each of the ADM dose-combinations administered bypatients of the patient population. Thereafter, the treatment mode ofthe ADM adjustment program 400 c may retrieve the decision tree from thenon-transitory memory 24, 114, 124 to allow the user 40 (e.g., physicalor medical professional) to prescribe an optimum ADM dose-combinationthat is personalized for a patient 10 being treated. For example, thetreatment mode of the ADM adjustment program 400 c may select an optimumADM dose-combination from one of blocks 484 a-484 c for a new orreturning patient that corresponds to the ADM dose-combinationassociated with the lowest MeanA1cNext obtained from the training dataof the training mode at block 486.

Referring to FIG. 5, a method 500 of determining a treatment dose ofinsulin for a treated patient 10 includes obtaining 502 training data310 for a plurality of patients 10 of a patient population at dataprocessing hardware 112, 132, 142, 192 from memory hardware 24, 114,134, 144, 194 in communication with the data processing hardware 112,132, 142, 192. The training data 310 includes training blood glucosehistory data 208 b and training patient-state information 208 a for eachpatient 10 of the patient population. The training blood glucose historydata 208 b includes treatment doses 316, 336 of insulin administered bythe patients 10 of the patient population and one or more outcomeattributes 318, 338 associated with each treatment dose 316 of insulin.The method 500 includes the data processing hardware 112, 132, 142, 192identifying 504, for each patient 10 of the patient population, one ormore optimum treatment doses 340 of insulin from the treatment doses316, 336 of insulin yielding favorable outcome attributes 318, 338.

The method 500 also includes receiving 506 patient-state information 208a for the treated patient 10 at the data processing hardware 112, 132,142, 192 and the data processing hardware 112, 132, 142, 192 determining508 a next recommended treatment dose of insulin for the treated patient10 based on one or more of the identified optimum treatment doses 340associated with patients 10 of the patient population having trainingpatient-state information 310, 208 a similar to the patient-stateinformation 208 a for the treated patient 10. The method 500 includestransmitting 510 the next recommended treatment dose to a portabledevice 110, 123, 124 associated with the treated patient. The portabledevice 110, 123, 124 may display the next recommended treatment dose.

Various implementations of the systems and techniques described here canbe realized in digital electronic and/or optical circuitry, integratedcircuitry, specially designed ASICs (application specific integratedcircuits), computer hardware, firmware, software, and/or combinationsthereof. These various implementations can include implementation in oneor more computer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms “machine-readable medium” and“computer-readable medium” refer to any computer program product,non-transitory computer readable medium, apparatus and/or device (e.g.,magnetic discs, optical disks, memory, Programmable Logic Devices(PLDs)) used to provide machine instructions and/or data to aprogrammable processor, including a machine-readable medium thatreceives machine instructions as a machine-readable signal. The term“machine-readable signal” refers to any signal used to provide machineinstructions and/or data to a programmable processor.

Implementations of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, or in computer software, firmware, or hardware, including thestructures disclosed in this specification and their structuralequivalents, or in combinations of one or more of them. Moreover,subject matter described in this specification can be implemented as oneor more computer program products, i.e., one or more modules of computerprogram instructions encoded on a computer readable medium for executionby, or to control the operation of, data processing apparatus. Thecomputer readable medium can be a machine-readable storage device, amachine-readable storage substrate, a memory device, a composition ofmatter effecting a machine-readable propagated signal, or a combinationof one or more of them. The terms “data processing apparatus”,“computing device” and “computing processor” encompass all apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, or multiple processors or computers.The apparatus can include, in addition to hardware, code that creates anexecution environment for the computer program in question, e.g., codethat constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, or a combination of one or moreof them. A propagated signal is an artificially generated signal, e.g.,a machine-generated electrical, optical, or electromagnetic signal, thatis generated to encode information for transmission to suitable receiverapparatus.

A computer program (also known as an application, program, software,software application, script, or code) can be written in any form ofprogramming language, including compiled or interpreted languages, andit can be deployed in any form, including as a stand-alone program or asa module, component, subroutine, or other unit suitable for use in acomputing environment. A computer program does not necessarilycorrespond to a file in a file system. A program can be stored in aportion of a file that holds other programs or data (e.g., one or morescripts stored in a markup language document), in a single filededicated to the program in question, or in multiple coordinated files(e.g., files that store one or more modules, sub programs, or portionsof code). A computer program can be deployed to be executed on onecomputer or on multiple computers that are located at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. The essential elements of a computer area processor for performing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto optical disks, or optical disks. However, a computerneed not have such devices. Moreover, a computer can be embedded inanother device, e.g., a mobile telephone, a personal digital assistant(PDA), a mobile audio player, a Global Positioning System (GPS)receiver, to name just a few. Computer readable media suitable forstoring computer program instructions and data include all forms ofnon-volatile memory, media and memory devices, including by way ofexample semiconductor memory devices, e.g., EPROM, EEPROM, and flashmemory devices; magnetic disks, e.g., internal hard disks or removabledisks; magneto optical disks; and CD ROM and DVD-ROM disks. Theprocessor and the memory can be supplemented by, or incorporated in,special purpose logic circuitry.

To provide for interaction with a user, one or more aspects of thedisclosure can be implemented on a computer having a display device,e.g., a CRT (cathode ray tube), LCD (liquid crystal display) monitor, ortouch screen for displaying information to the user and optionally akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's client device in response to requests received from the webbrowser.

One or more aspects of the disclosure can be implemented in a computingsystem that includes a backend component, e.g., as a data server, orthat includes a middleware component, e.g., an application server, orthat includes a frontend component, e.g., a client computer having agraphical user interface or a Web browser through which a user caninteract with an implementation of the subject matter described in thisspecification, or any combination of one or more such backend,middleware, or frontend components. The components of the system can beinterconnected by any form or medium of digital data communication,e.g., a communication network. Examples of communication networksinclude a local area network (“LAN”) and a wide area network (“WAN”), aninter-network (e.g., the Internet), and peer-to-peer networks (e.g., adhoc peer-to-peer networks).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someimplementations, a server transmits data (e.g., an HTML page) to aclient device (e.g., for purposes of displaying data to and receivinguser input from a user interacting with the client device). Datagenerated at the client device (e.g., a result of the user interaction)can be received from the client device at the server.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of the disclosure or of what maybe claimed, but rather as descriptions of features specific toparticular implementations of the disclosure. Certain features that aredescribed in this specification in the context of separateimplementations can also be implemented in combination in a singleimplementation. Conversely, various features that are described in thecontext of a single implementation can also be implemented in multipleimplementations separately or in any suitable sub-combination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multi-tasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

A number of implementations have been described. Nevertheless, it willbe understood that various modifications may be made without departingfrom the spirit and scope of the disclosure. Accordingly, otherimplementations are within the scope of the following claims. Forexample, the actions recited in the claims can be performed in adifferent order and still achieve desirable results.

What is claimed is:
 1. A method for determining a treatment dose for atreated patient, the method comprising: obtaining, at data processinghardware, training data for a plurality of patients of a patientpopulation from memory hardware in communication with the dataprocessing hardware, the training data comprising training blood glucosehistory data and training patient-state information for each patient ofthe patient population, the training blood glucose history datacomprising treatment meal boluses of insulin administered by eachpatient of the patient population during a scheduled time intervalwithin a day and an outcome attribute associated with each of thetreatment meal boluses of insulin administered by the patients of thepatient population during the scheduled time interval; for each patientof the patient population, identifying, using the data processinghardware, an optimum meal bolus of insulin associated with the scheduledtime interval that yields the most favorable output come attribute;receiving, at the data processing hardware, patient-state informationfor a treated patient; determining, using the data processing hardware,a next recommended treatment meal bolus of insulin for administrationduring the scheduled time interval for the treated patient based on oneor more of the identified optimum treatment meal boluses of insulinassociated with patients of the patient population having trainingpatient-state information similar to the patient-state information forthe treated patient; and administrating insulin to the treated patientduring the scheduled time interval by transmitting the next recommendedtreatment meal bolus of insulin from the data processing hardware to anadministration device, the next recommended treatment meal bolus ofinsulin when received from the data processing hardware causing theadministration device to administer the next recommended treatment mealbolus of insulin during the scheduled time interval.
 2. The method ofclaim 1, wherein obtaining the training data comprises obtaining thetraining data periodically at an end of a re-occurring configurable timeinterval.
 3. The method of claim 1, wherein obtaining the training datacomprises obtaining the training data immediately in response to a userinput selecting an immediate start button displayed upon a display incommunication with the data processing hardware.
 4. The method of claim1, wherein obtaining the training data comprises obtaining the trainingdata on a selected date.
 5. The method of claim 1, wherein determiningthe next recommended treatment meal bolus of insulin for the treatedpatient comprises: receiving meal boluses of insulin previouslyadministered by the treated patient during the scheduled time-interval;and determining the next recommended treatment meal bolus of insulinduring the scheduled time interval for the treated patient based on atleast one of the identified optimum meal boluses of insulin associatedwith the scheduled time interval and the received meal boluses ofinsulin previously administered by the treated patient during thescheduled time interval.
 6. The method of claim 5, wherein the scheduledtime interval comprises a pre-breakfast time interval, a pre-lunch timeinterval, a pre-dinner time interval, a bedtime time interval, or amidsleep time interval.
 7. The method of claim 1, wherein the outcomeattribute of the training blood glucose history data comprises a bloodglucose percent error based on a function of a next scheduled bloodglucose measurement and a blood glucose target range, the next scheduledblood glucose measurement corresponding to a blood glucose measurementoccurring after administration of a corresponding treatment meal bolusof insulin.
 8. The method of claim 7, wherein identifying the optimummeal bolus of insulin associated with the scheduled time interval thatyields the most favorable output come attribute comprises identifyingthe optimum meal bolus of insulin associated with the scheduled timeinterval that yields the lowest blood glucose percent error value. 9.The method of claim 1, wherein the outcome attribute of the trainingblood glucose history data comprises a mean blood glucose percent errorbased on a function of next scheduled blood glucose measurements and ablood glucose target range, each next scheduled blood glucosemeasurement corresponding to a blood glucose measurement occurring afteradministration of a corresponding treatment meal bolus of insulin. 10.The method of claim 9, wherein identifying the optimum meal bolus ofinsulin associated with the scheduled time interval that yields the mostfavorable output come attribute comprises identifying the optimum mealbolus of insulin associated with the scheduled time interval that yieldsthe lowest mean blood glucose percent error value.
 11. The method ofclaim 1, wherein the administration device comprises: a doser; and anadministration computing device in communication with the doser, theadministration computing device configured to automatically dial in anumber of units of insulin for the next recommended treatment meal bolusof insulin and cause the doser to administer the number of units ofinsulin for the next recommended treatment dose of insulin to thetreated patient during the scheduled time interval.
 12. The method ofclaim 1, wherein the training blood glucose history data furthercomprises anti-diabetes medication dose-combinations administered bypatients of the patient population that use anti-diabetes medicationsfor treatment and a glycated hemoglobin measurement associated with eachanti-diabetes medication dose-combination administered by the patientsof the patient population.
 13. The method of claim 12, furthercomprising: for each patient of the patient population usinganti-diabetes medications for treatment, identifying, using the dataprocessing hardware, an optimum anti-diabetes medicationdose-combinations that yields the most favorable glycated hemoglobinmeasurement; receiving, at the data processing hardware, newpatient-state information for a new treated patient, the newpatient-state information indicating that the new treated patient usesanti-diabetes medications for treating the new treated patient;determining, using the data processing hardware, a next recommendedanti-diabetes medication dose-combination for the new treated patientbased on one or more of the identified optimum anti-diabetes medicationdose-combinations associated with patients of the patient populationhaving training patient-state information similar to the newpatient-state information for the new treated patient; and transmittinga next recommended anti-diabetes medication dose-combination to aportable device associated with the new treated patient, the portabledevice configured to display the next recommended anti-diabetesmedication dose-combination.
 14. The method of claim 13, furthercomprising: receiving, at the data processing hardware, a glycatedhemoglobin measurement for the new treated patient, wherein determiningthe next recommended anti-diabetes medication dose-combination for thenew treated patient is further based on the glycated hemoglobinmeasurement for the new treated patient.
 15. The method of claim 1,wherein the patient-state information comprises a plurality ofpatient-state attributes associated with the patient, the patient-stateattributes including one or more of an age, a gender, a medical history,a body mass index, a medical history, risk factors, and/or financialattributes.
 16. A system comprising: a dosing controller including dataprocessing hardware and memory hardware in communication with the dataprocessing hardware, the dosing controller: obtaining training data fora plurality of patients of a patient population from memory hardware incommunication with the data processing hardware, the training datacomprising training blood glucose history data and trainingpatient-state information for each patient of the patient population,the training blood glucose history data comprising treatment mealboluses of insulin administered by each patient of the patientpopulation during a scheduled time interval within a day and an outcomeattribute associated with each of the treatment meal boluses of insulinadministered by the patients of the patient population during thescheduled time interval; for each patient of the patient population,identifying an optimum meal bolus of insulin associated with thescheduled time interval that yields the most favorable output comeattribute; receiving patient-state information for a treated patient;determining a next recommended treatment meal bolus of insulin foradministration during the scheduled time interval for the treatedpatient based on one or more of the identified optimum treatment mealboluses of insulin associated with patients of the patient populationhaving training patient-state information similar to the patient-stateinformation for the treated patient; and administrating insulin to thetreated patient during the scheduled time interval by transmitting thenext recommended treatment meal bolus of insulin to an administrationdevice, the next recommended treatment meal bolus of insulin whenreceived from the data processing hardware causing the administrationdevice to administer the next recommended treatment meal bolus ofinsulin during the scheduled time interval.
 17. The system of claim 16,wherein obtaining the training data comprises obtaining the trainingdata periodically at an end of a re-occurring configurable timeinterval.
 18. The system of claim 16, wherein obtaining the trainingdata comprises obtaining the training data immediately in response to auser input selecting an immediate start button displayed upon a displayin communication with the data processing hardware.
 19. The system ofclaim 16, wherein obtaining the training data comprises obtaining thetraining data on a selected date.
 20. The system of claim 16, whereindetermining the next recommended treatment meal bolus of insulin for thetreated patient comprises: receiving meal boluses of insulin previouslyadministered by the treated patient during the scheduled time-interval;and determining the next recommended treatment meal bolus of insulinduring the scheduled time interval for the treated patient based on atleast one of the identified optimum meal boluses of insulin associatedwith the scheduled time interval and the received meal boluses ofinsulin previously administered by the treated patient during thescheduled time interval.
 21. The system of claim 20, wherein thescheduled time interval comprises a pre-breakfast time interval, apre-lunch time interval, a pre-dinner time interval, a bedtime timeinterval, or a midsleep time interval.
 22. The system of claim 16,wherein the outcome attribute of the training blood glucose history datacomprises a blood glucose percent error based on a function of a nextscheduled blood glucose measurement and a blood glucose target range,the next scheduled blood glucose measurement corresponding to a bloodglucose measurement occurring after administration of a correspondingtreatment meal bolus of insulin.
 23. The system of claim 22, whereinidentifying the optimum meal bolus of insulin associated with thescheduled time interval that yields the most favorable output comeattribute comprises identifying the optimum meal bolus of insulinassociated with the scheduled time interval that yields the lowest bloodglucose percent error value.
 24. The system of claim 16, wherein theoutcome attribute of the training blood glucose history data comprises amean blood glucose percent error based on a function of next scheduledblood glucose measurements and a blood glucose target range, each nextscheduled blood glucose measurement corresponding to a blood glucosemeasurement occurring after administration of a corresponding treatmentmeal bolus of insulin.
 25. The system of claim 24, wherein identifyingthe optimum meal bolus of insulin associated with the scheduled timeinterval that yields the most favorable output come attribute comprisesidentifying the optimum meal bolus of insulin associated with thescheduled time interval that yields the lowest mean blood glucosepercent error value.
 26. The system of claim 16, wherein theadministration device comprises: a doser; and an administrationcomputing device in communication with the doser, the administrationcomputing device configured to automatically dial in a number of unitsof insulin for the next recommended treatment meal bolus of insulin andcause the doser to administer the number of units of insulin for thenext recommended treatment dose of insulin to the treated patient duringthe scheduled time interval.
 27. The system of claim 16, wherein thetraining blood glucose history data further comprises anti-diabetesmedication dose-combinations administered by patients of the patientpopulation that use anti-diabetes medications for treatment and aglycated hemoglobin measurement associated with each anti-diabetesmedication dose-combination administered by the patients of the patientpopulation.
 28. The system of claim 27, wherein the dosing controller:for each patient of the patient population using anti-diabetesmedications for treatment, identifies an optimum anti-diabetesmedication dose-combinations that yields the most favorable glycatedhemoglobin measurement; receives new patient-state information for a newtreated patient, the new patient-state information indicating that thenew treated patient uses anti-diabetes medications for treating the newtreated patient; determines a next recommended anti-diabetes medicationdose-combination for the new treated patient based on one or more of theidentified optimum anti-diabetes medication dose-combinations associatedwith patients of the patient population having training patient-stateinformation similar to the new patient-state information for the newtreated patient; and transmits a next recommended anti-diabetesmedication dose-combination to a portable device associated with the newtreated patient, the portable device configured to display the nextrecommended anti-diabetes medication dose-combination.
 29. The system ofclaim 28, wherein the dosing controller: receives a glycated hemoglobinmeasurement for the new treated patient, wherein determining the nextrecommended anti-diabetes medication dose-combination for the newtreated patient is further based on the glycated hemoglobin measurementfor the new treated patient.
 30. The system of claim 16, wherein thepatient-state information comprises a plurality of patient-stateattributes associated with the patient, the patient-state attributesincluding one or more of an age, a gender, a medical history, a bodymass index, a medical history, risk factors, and/or financialattributes.